diff --git a/05.04-regression.html b/05.04-regression.html index 0c1b47cd..28734dcc 100644 --- a/05.04-regression.html +++ b/05.04-regression.html @@ -734,13 +734,6 @@ <h2> Contents </h2> </pre></div> </div> </div> -<div class="cell_output docutils container"> -<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/Users/ethan/opt/miniconda3/envs/pythonbook3/lib/python3.11/site-packages/outdated/utils.py:14: OutdatedPackageWarning: The package pingouin is out of date. Your version is 0.5.3, the latest is 0.5.4. -Set the environment variable OUTDATED_IGNORE=1 to disable these warnings. - return warn( -</pre></div> -</div> -</div> </div> <div class="cell docutils container"> <div class="cell_input docutils container"> @@ -3021,14 +3014,11 @@ <h3><span class="section-number">16.9.1. </span>Three kinds of residuals<a class <span class="n">mod2</span> <span class="o">=</span> <span class="n">pg</span><span class="o">.</span><span class="n">linear_regression</span><span class="p">(</span><span class="n">predictors</span><span class="p">,</span> <span class="n">outcome</span><span class="p">,</span> <span class="n">as_dataframe</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> -<span class="c1">#df_slplot = pd.DataFrame(</span> -<span class="c1"># {'fitted': mod2['pred'],</span> -<span class="c1"># 'sqrt_abs_stand_res': np.sqrt(np.abs(mod2['residuals']))</span> -<span class="c1"># })</span> +<span class="c1"># Thanks to Ivan Savov (https://github.com/ivanistheone) for contributing this solution for getting the standardized residuals</span> <span class="n">SS_resid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">mod2</span><span class="p">[</span><span class="s2">"residuals"</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">mod2</span><span class="p">[</span><span class="s1">'residuals'</span><span class="p">])</span> -<span class="n">p</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1"># bcs 2 predictors = 'dan_sleep' and 'baby_sleep' </span> +<span class="n">p</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">predictors</span><span class="p">))</span> <span class="n">sigmahat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span> <span class="n">SS_resid</span><span class="o">/</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="n">p</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="p">))</span> <span class="n">stand_res</span> <span class="o">=</span> <span class="n">mod2</span><span class="p">[</span><span class="s1">'residuals'</span><span class="p">]</span> <span class="o">/</span> <span class="n">sigmahat</span> <span class="n">df_slplot</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span> @@ -3189,10 +3179,10 @@ <h3><span class="section-number">16.9.1. </span>Three kinds of residuals<a class <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 215.2</td> </tr> <tr> - <th>Date:</th> <td>Fri, 26 Apr 2024</td> <th> Prob (F-statistic):</th> <td>2.15e-36</td> + <th>Date:</th> <td>Thu, 02 May 2024</td> <th> Prob (F-statistic):</th> <td>2.15e-36</td> </tr> <tr> - <th>Time:</th> <td>20:17:20</td> <th> Log-Likelihood: </th> <td> -287.48</td> + <th>Time:</th> <td>11:13:42</td> <th> Log-Likelihood: </th> <td> -287.48</td> </tr> <tr> <th>No. Observations:</th> <td> 100</td> <th> AIC: </th> <td> 581.0</td> @@ -3257,10 +3247,10 @@ <h3><span class="section-number">16.9.1. </span>Three kinds of residuals<a class <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 201.7</td> </tr> <tr> - <th>Date:</th> <td>Fri, 26 Apr 2024</td> <th> Prob (F-statistic):</th> <td>2.78e-35</td> + <th>Date:</th> <td>Thu, 02 May 2024</td> <th> Prob (F-statistic):</th> <td>2.78e-35</td> </tr> <tr> - <th>Time:</th> <td>20:17:20</td> <th> Log-Likelihood: </th> <td> -287.48</td> + <th>Time:</th> <td>11:13:42</td> <th> Log-Likelihood: </th> <td> -287.48</td> </tr> <tr> <th>No. Observations:</th> <td> 100</td> <th> AIC: </th> <td> 581.0</td> @@ -3724,10 +3714,10 @@ <h3><span class="section-number">16.10.1. </span>Backward elimination<a class="h <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 142.2</td> </tr> <tr> - <th>Date:</th> <td>Fri, 26 Apr 2024</td> <th> Prob (F-statistic):</th> <td>3.42e-35</td> + <th>Date:</th> <td>Thu, 02 May 2024</td> <th> Prob (F-statistic):</th> <td>3.42e-35</td> </tr> <tr> - <th>Time:</th> <td>20:17:20</td> <th> Log-Likelihood: </th> <td> -287.43</td> + <th>Time:</th> <td>11:13:42</td> <th> Log-Likelihood: </th> <td> -287.43</td> </tr> <tr> <th>No. Observations:</th> <td> 100</td> <th> AIC: </th> <td> 582.9</td> diff --git a/_sources/05.04-regression.ipynb b/_sources/05.04-regression.ipynb index c03c0706..1f4a31b8 100644 --- a/_sources/05.04-regression.ipynb +++ b/_sources/05.04-regression.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "56714750", "metadata": { "editable": true, @@ -47,88 +47,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>dan_sleep</th>\n", - " <th>baby_sleep</th>\n", - " <th>dan_grump</th>\n", - " <th>day</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>7.59</td>\n", - " <td>10.18</td>\n", - " <td>56</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " 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"editable": true, @@ -166,21 +85,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sleepycorrelation-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from myst_nb import glue\n", "from matplotlib import pyplot as plt\n", @@ -237,7 +142,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "74508823", "metadata": { "editable": true, @@ -248,21 +153,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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oZ599Vu3vYWFhAKDqcHT06FGkpqZi1KhRKCwsVP0UFxfjmWeewcmTJ8tdNqXJ8OHD1b46rlevHiIjI1Vfmd+4cQNXrlxRXVNdel+9evXCgwcPVJejtW/fHn///Tc++OAD7N+/H7m5uTofb3Fxsdq2i4qKdF7XVCr7Nzh27Bjy8/MxdOhQteWeeOIJrZerlPXUU0/B3d1d9fe8vDzs2bMHAwYMgKOjY7nXPC8vD8eOHQMAHDhwAM2bN0fTpk3Vtjls2DCN+3J3d8dTTz2lNm3Lli1o3rw5WrVqpbavHj16qF0eUnJp49ChQ/H7778jPj6+3Pbbt2+Ps2fPYvz48dixYwcyMjIqPf6YmBjk5uaWu7wjMDAQTz31VLnLYkQiEfr27as2LSwsTGP3r6qo7N9/x44dKCwsxMiRI9VeN6lUik6dOqld9khkKvb29pg1axZOnTql8XImAKrGQWV/x4YMGQInJye9LtfWV3X9HpW97ycyMhL16tVTZZk+mdm+fXssX74cs2bNwrFjx1BQUKDz8Zbcm1N6++ZW2b/BgQMHAKBclg0ePBh2drrfCv/ss89CLBar/q7P+4cDBw7gqaeeglwuV61vY2NTrqbSx9CwYUO1aVu2bEGXLl3g7++vtq+ePXuqHWf79u2Rnp6OYcOGYfPmzUhOTi63fUPez5T8Xyv7e9a+fXs0adKk3O+Zr68v2rdvX+64qiPL8vLykJSUBODx6yYSifDCCy+ovW6+vr5o2bJlud9BmUxmVQ2wOFCqoTw9PdX+XnJDZMkv68OHDwE8PpGJxWK1n3nz5kEQBKSmpla6H19fX43TUlJS1Pbz7rvvltvP+PHjAUB1kvnmm28wefJkbNq0CV26dIGHhwf69++P69evV1rH2LFj1bbdtWvXStcxtcr+DUpeIx8fn3LrappWkbLd31JSUlBYWIiFCxeWe8179eoF4N/XPCUlRa/9a+o09/DhQ5w7d67cvlxcXCAIgmpf0dHR2LRpk+pNTZ06ddC8eXO1a7anTJmCL774AseOHUPPnj3h6emJrl27am3dW/I6aqrN399fNb+Eo6MjpFKp2jSJRIK8vLwK92EIXX8H27VrV+61W7t2rcbwJTKF5557Dq1bt8bUqVM1vqlPSUmBnZ1duSYqIpFI7XxvCtX1e6RrlumSmWvXrsWoUaOwZMkSdOjQAR4eHhg5ciQSExMrraNr165q2y59D6e5GJpldnZ25dbVpuw5XJ/3D8bKsr/++qvcvpo1a6a2rxEjRmDp0qW4c+cOBg0aBG9vb0RERGDXrl2qbRnyfkbfLNP02kokEr0+ZNaFLr+DgiDAx8en3Gt37Nixcr+Dbm5uVjVQYte7WqrkU5eFCxdW2OlElzfrmk78iYmJql+skv1MmTIFAwcO1LiNRo0aAQCcnJwwY8YMzJgxAw8fPlR9GtO3b19cuXJFax3Tp09X62rn4uJSae3mVvIalYRBaYmJiTp/q1T2ZlB3d3fY2tpixIgReP311zWuExwcrKqhov3rsi/g8b+xg4MDli5dqnGd0p/w9evXD/369UN+fj6OHTuGOXPmYPjw4QgKCkKHDh1gZ2eHSZMmYdKkSUhPT8fu3bvx4YcfokePHrh3757GDnslr+ODBw/KzUtISFDbvyUpqWvdunWoV6+emauh2kwkEmHevHl4+umn8eOPP5ab7+npicLCQjx69EhtsCQIAhITE1XfFpuDsX6PKsqykJAQtf3okplyuRwLFizAggULcPfuXfz555/44IMPkJSUhO3bt2utY/HixcjMzFT93VLPX6WVzrKAgADV9MLCQr0G0WXzRZ/3D8bKsrCwMHz22Wca1/H391f9ecyYMRgzZgyys7Nx8OBBTJs2DX369MG1a9dQr149g97PlM6yst3wLD3LRCIRDh06pLFLYdlpS5Ysqa7SjIIDpVoqKioKbm5uuHTpUrm22fpYs2YNJk2apDrp3LlzB0ePHsXIkSMBPD6JhYaG4uzZs5g9e7bO2/Xx8cHo0aNx9uxZLFiwQNWGuvQnGaVbeQYFBel1uZomZT8lqWiesVqIRkREQCKRYO3atWohcOzYMdy5c8fg43F0dESXLl0QGxuLsLAw2NvbV7hsp06d8MUXX+DSpUtql9/99ttvOu+vT58+mD17Njw9PVUDsMpIJBJ06tQJbm5u2LFjB2JjY9GhQwe1Zdzc3DB48GDEx8dj4sSJuH37drlLBAGgQ4cOcHBwwKpVq1TdngDg/v372Lt3LwYPHqzzsVSnHj16wM7ODjdv3sSgQYPMXQ7Vct26dcPTTz+NmTNnIjAwUG1e165dMX/+fKxatQpvv/22avr69euRnZ2t9g2+vp9oV/UTcGP9Hv36669q6x89ehR37tzBiy++CMDwzKxbty4mTJiAPXv24MiRI6rpFR13yRv/qqgsy4z9jUN0dDSAx9+ktW7dWjV93bp15brF6UOf9w+dOnXCtm3bkJycrBpQFBcX448//tB5f3369MG2bdvQoEEDtcvZtXFyckLPnj2hVCrRv39/XLx4sdyAvaL3M2WVXNa+atUqtQ8fTp48icuXL2Pq1Kk6H0t16tOnD+bOnYv4+PgKL3W0Zhwo1VLOzs5YuHAhRo0ahdTUVAwePBje3t549OgRzp49i0ePHuGHH36odDtJSUkYMGAAXnrpJSgUCkybNg1SqRRTpkxRLbN48WL07NkTPXr0wOjRoxEQEIDU1FRcvnwZp0+fVp3IIiIi0KdPH4SFhcHd3R2XL1/GypUr0aFDB9VJpUWLFgCAefPmoWfPnrC1ta10MKCrBg0awMHBAb/++iuaNGkCZ2dn+Pv7w9/f3yT79fDwwKRJkzBnzhy4u7tjwIABuH//PmbMmAE/Pz/Y2Bh+ZezXX3+Njh074sknn8Rrr72GoKAgZGZm4saNG/jrr79U9xxMnDgRS5cuRc+ePTFz5kz4+Phg9erVqk+8dKlh4sSJWL9+PaKjo/H2228jLCwMxcXFuHv3Lnbu3Il33nkHERER+OSTT3D//n107doVderUQXp6Or7++muIxWJ06tQJANC3b180b94cbdu2hZeXF+7cuYMFCxagXr16CA0N1bh/Nzc3fPzxx/jwww8xcuRIDBs2DCkpKZgxYwakUimmTZtm8OtoSkFBQZg5cyamTp2KW7du4ZlnnoG7uzsePnyIEydOqD6RJKou8+bNQ5s2bZCUlKS63AgAnn76afTo0QOTJ09GRkYGoqKicO7cOUybNg3h4eEYMWKEatkWLVrgt99+w9q1a1G/fn1IpVLV+VOTFi1aYP/+/fjrr7/g5+cHFxcXvQYLxvo9OnXqFF588UUMGTIE9+7dw9SpUxEQEKC6xEvXzFQoFOjSpQuGDx+Oxo0bw8XFBSdPnsT27dvVPhBr0aIFNmzYgB9++AFt2rSBjY0N2rZtq/Nxa9O8eXMAwI8//ggXFxdIpVIEBwfD09PTJPtt1qwZhg0bhi+//BK2trZ46qmncPHiRXz55ZeQyWRVyjJd3z9MnToVf/31F7p27YqpU6fCwcEBixYtUt03pksNM2fOxK5duxAZGYk333wTjRo1Ql5eHm7fvo1t27Zh0aJFqFOnDl566SU4ODggKioKfn5+SExMxJw5cyCTyVQDHF3ez5TVqFEjvPzyy1i4cCFsbGzQs2dP3L59Gx9//DECAwPVPqSwJFFRUXj55ZcxZswYnDp1CtHR0XBycsKDBw9w+PBhtGjRAq+99ppq+a5duyIlJQVnzpwxX9H6MGsrCTKILl3v/vjjD7XpmjqaCIIgHDhwQOjdu7fg4eEhiMViISAgQOjdu3e59Svaz8qVK4U333xT8PLyEiQSifDkk08Kp06dKrf82bNnhaFDhwre3t6CWCwWfH19haeeekpYtGiRapkPPvhAaNu2reDu7i5IJBKhfv36wttvvy0kJyerlsnPzxdefPFFwcvLSxCJROW62ejzepXt+iIIj7v0NG7cWBCLxWodgLTtt6Kud7r8GxQXFwuzZs0S6tSpI9jb2wthYWHCli1bhJYtW5brwqQJAOH111/XOC8uLk4YO3asEBAQIIjFYsHLy0uIjIxUdXEqceHCBaFbt26CVCoVPDw8hHHjxgkrVqwQAAhnz55VLaetpWdWVpbw0UcfCY0aNRLs7e1VrcnffvttVWvQLVu2CD179hQCAgIEe3t7wdvbW+jVq5dw6NAh1Xa+/PJLITIyUpDL5YK9vb1Qt25dYdy4ccLt27dVy2hqDy4IgrBkyRIhLCxMtf9+/fqV6+ZX0f8FTW1aNdGn613ZLoAl/y/KdqLatGmT0KVLF8HV1VWQSCRCvXr1hMGDB1faop/IUBX9HxUEQRg+fLgAoNzvem5urjB58mShXr16glgsFvz8/ITXXntNrX22IDzufNq9e3fBxcVFAFBpZ7czZ84IUVFRgqOjo1q3z+r6PSrZz86dO4URI0YIbm5uqi6a169fL7d8ZZmZl5cnvPrqq0JYWJjg6uoqODg4CI0aNRKmTZsmZGdnq7aTmpoqDB48WHBzc1NlSmV07XonCIKwYMECITg4WLC1tVXLHW37rcq5LC8vT5g0aZLg7e0tSKVS4YknnhBiYmIEmUwmvP3221qPqyQbP//8c43zdXn/IAiCcOjQISEiIkKQSCSCr6+v8N577wnz5s0r1221Xr16Qu/evTXu69GjR8Kbb74pBAcHC2KxWPDw8BDatGkjTJ06VcjKyhIEQRBWrFghdOnSRfDx8RHs7e0Ff39/YejQocK5c+dU29Hl/Yym3CkqKhLmzZsnNGzYUBCLxYJcLhdeeOEF4d69e2rLVfR/YdSoUTp1U9Sn613pToKCUHEGL126VIiIiBCcnJwEBwcHoUGDBsLIkSPLvSe0tvbgIkEQBBOPxYhID3FxcWjcuDGmTZum9gC76vTyyy9jzZo1SElJMcq3dUREVLscPXoUUVFR+PXXXzF8+HCz1NC9e3fcvn0b165dM8v+yfrx0jsiMzp79izWrFmDyMhIuLq64urVq5g/fz5cXV0xbty4aqlh5syZ8Pf3R/369ZGVlYUtW7ZgyZIl+OijjzhIIiKiSu3atQsxMTFo06YNHBwccPbsWcydOxehoaEVNmIwtkmTJiE8PByBgYFITU3Fr7/+il27duHnn3+ulv1TzcSBEpEZOTk54dSpU/j555+Rnp4OmUyGzp0747PPPtOrRXhViMVifP7557h//z4KCwsRGhqKr776Cm+99Va17J+IiKybq6srdu7ciQULFiAzMxNyuRw9e/bEnDlzyj2OwVSKiorwySefIDExESKRCE2bNsXKlSvxwgsvVMv+qWbipXdERERERERl8IGzREREREREZXCgREREREREVAYHSkRERERERGXU+IGSIAjIyMgAb8UiIiJLwWwiIrJ8NX6glJmZCZlMhszMTHOXQkREBIDZRERkDWr8QImIiIiIiEhfHCgRERERERGVwYESERERERFRGRwoERERERERlcGBEhERERERURkcKBEREREREZXBgRIREREREVEZHCgRERERERGVwYESERERERFRGRwoERERERERlcGBEhERERERURkcKBEREREREZVhZ+4CyLQUOUokZymRkVcAVwcx5E72kDnam7ssIiKqpZhLRGQtOFCqwRLSczF5/Tkcup6smhYdKsfcQWHwd3MwY2VERFQbMZeIyJrw0rsaSpGjLBdGAHDwejI+WH8OihylmSojIqLaiLlERNaGA6UaKjlLWS6MShy8nozkLAYSERFVH+YSEVkbDpRqqIy8Aq3zMyuZT0REZEzMJSKyNhwo1VCuUrHW+S6VzCciIjIm5hIRWRsOlGooubM9okPlGudFh8ohd2aHISIiqj7MJSKyNmYdKGVmZmLixImoV68eHBwcEBkZiZMnT6rmC4KA6dOnw9/fHw4ODujcuTMuXrxYrTUKgoD8wqJq3acxyBztMXdQWLlQig6VY96gMLZiJSKiasVcIiJrIxIEQTDXzv/zn//gwoUL+OGHH+Dv749Vq1bhv//9Ly5duoSAgADMmzcPn332GZYvX46GDRti1qxZOHjwIK5evQoXFxed9pGRkQGZTAaFQgFXV1e96issKsaHG88jNVuJH15oA7Gt9X0BV/K8isy8ArhIxZA783kVRETmVpVssnbMJSKyFmYbKOXm5sLFxQWbN29G7969VdNbtWqFPn364NNPP4W/vz8mTpyIyZMnAwDy8/Ph4+ODefPm4ZVXXtFpP1UJo8sPMtD/uyPILyzGwPAAfDGkJWxsRHptg4iIqKzaPFAiIrIWZvuKpLCwEEVFRZBKpWrTHRwccPjwYcTFxSExMRHdu3dXzZNIJOjUqROOHj1a4Xbz8/ORkZGh9mOoJn6u+P751rC1EWFDbDxmbrkEM34BR0REVsqY2URERNXDbAMlFxcXdOjQAZ9++ikSEhJQVFSEVatW4fjx43jw4AESExMBAD4+Pmrr+fj4qOZpMmfOHMhkMtVPYGBglers2sQHXw5pCQBYfvQ2vt5zvUrbIyKi2sfY2URERKZn1ptuVq5cCUEQEBAQAIlEgm+++QbDhw+Hra2tahmRSP1SN0EQyk0rbcqUKVAoFKqfe/fuVbnO/uEBmPFsMwDAgt3XsfxIXJW3SUREtYcpsomIiEzLzpw7b9CgAQ4cOIDs7GxkZGTAz88P//nPfxAcHAxfX18AQGJiIvz8/FTrJCUllfuWqTSJRAKJRGL0WkdFBiE9pwD/3X0N0/+6BJmjGAPC6xh9P1VVcpNsRl4BXB3EkDvxJlkiInMzVTZZC2YTEVkjsw6USjg5OcHJyQlpaWnYsWMH5s+frxos7dq1C+Hh4QAApVKJAwcOYN68eWap882uIUjPVWLZkdt4949zcJGI0a1pxYO26paQnovJ68/h0PVk1bToUDnmDgqDv5uDGSsjIqLaitlERNbKrJfe7dixA9u3b0dcXBx27dqFLl26oFGjRhgzZgxEIhEmTpyI2bNnY+PGjbhw4QJGjx4NR0dHDB8+3Cz1ikQifNy7KQa2DkBRsYDXV5/GsVspZqmlLEWOslwQAcDB68n4YP05KHKUZqqMiIhqK2YTEVkzs36jpFAoMGXKFNy/fx8eHh4YNGgQPvvsM4jFYgDA+++/j9zcXIwfPx5paWmIiIjAzp07dX6GkinY2Igwf1AYMnILsfvyQ7y44hR+e/kJNA+Qma0mAEjOUpYLohIHrycjOUvJyxyIiKhaMZuIyJqZ9YGz1cFUz6rIKyjC6GUncOxWKjyc7PHHqx3QwMvZaNvXV+zdNAz4vuK26ZvGR6JVXfdqrIiIiCpSW56jxGwiImtm1kvvrJlUbIufRrZFiwAZUrOVGLHkOBLSc81Wj6tUrHW+SyXziYiIjI3ZRETWjAOlKnCRirF8TDvU93JCgiIPL/x8HClZ+WapRe5sj+hQucZ50aFyyJ15aQMREVUvZhMRWTMOlKrI01mCVeMi4C+T4tajbIxadgKZeQXVXofM0R5zB4WVC6ToUDnmDQrjNeBERFTtmE1EZM14j5KR3HyUhaGLYpCSrUREsAdWjG0Pqdi28hWNrORZFZl5BXCRiiF35rMqiIgsTW25R6kEs4mIrBEHSkZ0IV6B5348hqz8QnRr4o0fXmgDsS2/tCMiInW1baBERGSN+C7eiJoHyLBkVFtI7Gyw+3ISJq87h+LiGj0OJSIiIiKqkThQMrIn6nvi++dbw9ZGhA2x8Zi55RJq+Jd2REREREQ1DgdKJtC1iQ++HNISALD86G18s+eGmSsiIiIiIiJ9cKBkIv3DAzC9b1MAwH93X8PyI3FmroiIiIiIiHTFgZIJjY4KxtvdGgIApv91CZti481cERERERER6YIDJRN7s2sIRkcGAQDe+eMsdl96aN6CiIiIiIioUhwomZhIJMInfZpiYHgAiooFvL76NI7dSjF3WUREREREpAUHStXAxkaEeYPD0K2JD/ILi/HiilO4EK8wd1lERERERFQBDpSqidjWBt8OD8cT9T2QlV+IkUtP4OajLHOXRUREREREGnCgVI2kYlv8NLItWgTIkJqtxIglx5GQnltt+1fkKHEzKQuxd9Nw81EWFDnKats3ERGRJswmIrJUIqGGPw01IyMDMpkMCoUCrq6u5i4HAJCSlY8hi2Nw61E26ns54Y9XOsDTWWLSfSak52Ly+nM4dD1ZNS06VI65g8Lg7+Zg0n0TEZE6S8wmc2A2EZEl4zdKZuDpLMGqcRHwl0lx61E2Ri07gcy8ApPtT5GjLBdEAHDwejI+WH+On94REVG1YzYRkaXjQMlM/N0csPLFCHg62eNCfAZeXHEKeQVFJtlXcpayXBCVOHg9GclZDCMiIqpezCYisnQcKJlRAy9nrBjbHs4SOxyPS8WE1adRUFRs9P1kVPJtlSm/zSIiItKE2URElo4DJTNrHiDDklFtIbGzwe7LSXh/3TkUFxv3tjFXqVjrfJdK5hMRERkbs4mILB0HShbgifqe+G54a9jaiLAxNh4zt1yCMXtsyJ3tER0q1zgvOlQOubO90fZFRESkC2YTEVk6DpQsRLemPvhiSBgAYPnR2/h6z3WjbVvmaI+5g8LKBVJ0qBzzBoVB5sgwIiKi6sVsIiJLx/bgFmb5kThM/+sSAGBa36YYExVstG0rcpRIzlIiM68ALlIx5M72DCIiIjOwtmwyJWYTEVkqO3MXQOpGRwUjPbcAC3Zfx4y/LsHNUYwB4XWMsm2ZI8OHiIgsC7OJiCwVL72zQG91DcXoyCAAwLt/nMPuSw/NWxARERERUS3DgZIFEolE+KRPUwwID0BRsYDxq0/j2K0Uc5dFRERERFRrcKBkoWxsRJg/OAzdmnhDWViMF1ecwvn7CnOXRURERERUK3CgZMHEtjb4dnhrRAR7ICu/EKOWncCNpCxzl0VEREREVONxoGThpGJbLBnVFs0DXJGarcTIn48jPj3X3GUREREREdVoHChZARepGCvGtEd9LyckKPIw4ufjSMnKN3dZREREREQ1FgdKVsLTWYKV4yLgJ5Pi1qNsjFp2Apl5BeYui4iIiIioRuJAyYoEuDlg5bgIeDjZ40J8Bl5ccQp5BUXmLouIiIiIqMbhQMnKhHg7Y8WY9nCW2OF4XComrD6NgqJic5dFRERERFSjcKBkhVrUkWHJqLawt7PB7stJmLzuHIqLBXOXRURERERUY3CgZKWeqO+J74a3hq2NCBti4zFzyyUIAgdLRERERETGwIGSFXu6qQ8+HxwGAFh+9Da+2XPDzBUREREREdUMHChZuYGt62Ba36YAgP/uvoblR+LMXBERERERkfXjQKkGGBMVjIndQgEA0/+6hE2x8WauiIiIiIjIunGgVEO81TUUoyODAADv/HEWuy89NG9BRERERERWjAOlGkIkEuGTPk0xIDwARcUCXl99GsdvpZi7LCIiIiIiq2Rn7gLIeGxsRJg/OAyp2fk4cC0ZY5afxJdDWyKyvidkjvY6bUORo0RylhIZeQVwdRBD7mRf6bql13GW2MHe1gbpuUo4S3Vbn4iIaj5D8sXQdcsu7yyxQ3Z+IRS5+u+biGovDpRqmEeZ+SgoetwmPEdZhNdWnUbbem74Zlhr+Ls5aF03IT0Xk9efw6Hryapp0aFyzB0UVuG6mtaJCvHEmKhgDPvpONrWc9e6PhER1XyG5Iuh62pavmOIJ0ZHBePNNbHIURbpvG8iqt146V0NoshRYvL6czh6U/2Su1N30jHxt1gocpSVrls6WADg4PVkfLD+nMZ1K1rnyI0ULDsSh7Edg7WuT0RENZ8h+WLouhUtf7hULum6byIiDpRqkOQsZblwKHHidhpuJGUZtO7B68lIziofJtrWOXIjBeGBblrXJyKims+QfDF0XV1zSZd9ExFxoFSDZOQVaJ3//vpzyKxgmcrW1bReZevkFxZrXZ+IiGo+Q/LF0HX1yaXK9k1ExIFSDeIqFWudf/NRNl765RTyCor0XtdFw/zK1pHY/fvfS9P6RERU8xmSL4auq08uVbZvIiIOlGoQubM9okPlGueFB7rByd4Wx26lYsLqWBQWFeu8bnSoHHLn8t2BtK0TFeKJ2HvpWtcnIqKaz5B8MXRdXXNJl30TEXGgVIPIHO0xd1BYuZCIDpXj++db4+fR7WBvZ4Pdlx/i/fXnUFws6LTuvEFhGtuoVrROSde7pYfjtK5PREQ1nyH5Yui6FS3fsVQu6bpvIiKRIAhC5YtZr4yMDMhkMigUCri6upq7nGpR8vyIzLwCuEjFkDv/+7yI3Zce4pVV/6CoWMDoyCBM69sUIpFIp3V12Z/T/5+jpMhVwkmi2/pERLUNs0m/fNB33bLLO0sfP0cpI1f/fRNR7cWBUi20MfY+3l57FgAw6emGeLNrqJkrIiKqXZhNRESWj5fe1UIDwutgWt+mAICvdl3DiqO3zVsQEREREZGF4UCplhoTFYyJ3R5/kzTtz4vYFBtv5oqIiIiIiCwHB0q12FtdQzE6MggA8M4fZ7Hn8kPzFkREREREZCHMOlAqLCzERx99hODgYDg4OKB+/fqYOXMmiov/bV0tCAKmT58Of39/ODg4oHPnzrh48aIZq645RCIRPunTFL1b+KGoWMBrq05jY+x9KHL4pHIiIjIvRY4SN5OyEHs3DTcfZTGbiKja2Zlz5/PmzcOiRYuwYsUKNGvWDKdOncKYMWMgk8nw1ltvAQDmz5+Pr776CsuXL0fDhg0xa9YsPP3007h69SpcXFzMWX6NkJiRh/Tcx+GjLCrG22vPonWgG759vjX83RzMXB0REdVGCem5mLz+HA5dT1ZNiw6VY+6gMGYTEVUbs36jFBMTg379+qF3794ICgrC4MGD0b17d5w6dQrA42+TFixYgKlTp2LgwIFo3rw5VqxYgZycHKxevdqcpdcIihwlJq8/hyM3UtSmn76XjjfXxPLTOyIiqnYl2VR6kAQAB68n44P155hNRFRtzDpQ6tixI/bs2YNr164BAM6ePYvDhw+jV69eAIC4uDgkJiaie/fuqnUkEgk6deqEo0ePatxmfn4+MjIy1H5Is+QsZbkgKnHqThouPeBrR0RkDMwm3WnLpoPXk5GcxYESEVUPsw6UJk+ejGHDhqFx48YQi8UIDw/HxIkTMWzYMABAYmIiAMDHx0dtPR8fH9W8subMmQOZTKb6CQwMNO1BWLGMvAKt89/74xxSsxlIRERVxWzSXWXZlFnJfCIiYzHrQGnt2rVYtWoVVq9ejdOnT2PFihX44osvsGLFCrXlRCKR2t8FQSg3rcSUKVOgUChUP/fu3TNZ/dbOVSrWOv9+ei5GLzvBUCIiqiJmk+4qyyaXSuYTERmLWZs5vPfee/jggw/w3HPPAQBatGiBO3fuYM6cORg1ahR8fX0BPP5myc/PT7VeUlJSuW+ZSkgkEkgkEtMXXwPIne0RHSrHQQ2XOLSt54abj7Jx7r4CL/1yCsvHtIdUbAsAeJiRh7RsJTLyCuHqYAd3R3v4uEqru3wiIqvBbNKdtmyKDpVD7myvcT1mExEZm1m/UcrJyYGNjXoJtra2qvbgwcHB8PX1xa5du1TzlUolDhw4gMjIyGqttSaSOdpj7qAwRIfK1aZHh8qxcFhrrBwXAWeJHY7dSsWE1bEoLCrG3ZRsTPr9DJ75+hCGLo7BMwsO4Z3fz+BuSraZjoKIiGoSbdk0b1AYZI7lB0rMJiIyBZEgCIK5dj569Gjs3r0bixcvRrNmzRAbG4uXX34ZY8eOxbx58wA8biE+Z84cLFu2DKGhoZg9ezb279+vc3vwjIwMyGQyKBQKuLq6mvqQrJIiR4nkLCUy8wrgIhVD7myvCqJjt1IwcukJKAuL0auFL9JzlDh6M7XcNjqGeOLLoa346R0RkQ6YTZXTlk2lPczIw6Tfz5Tr4Aowm4ioasx66d3ChQvx8ccfY/z48UhKSoK/vz9eeeUVfPLJJ6pl3n//feTm5mL8+PFIS0tDREQEdu7cyWcoGZHMUXP4AMAT9T3x/fDWeGXVP9h2XnMDDQA4fCMFadlKhhERERmFtmwqLS1bqXGQBDCbiKhqzPqNUnXgp3bGsTH2Pt5ee1brMr+/0gHtgz2qqSIiIuvFbDKeE3GpGLo4psL5zCYiMpRZ71Ei6zEgvA5e7VRf6zKuUrN+QUlERLVQZdnDbCIiQ3GgRDobExWMuh4OGud1DPGEu1Pll0gQEREZk7uTPTqGeGqcx2wioqrgQIl05uMqxcqx7eEnU7/Wu2OIJ2YPaFHla8AVOUrcTMpC7N003HyUBUUOH3ZLRETa+bhKMXtAi3KDJWYTEVUV71EivT1Q5GLqxgvYeyUJ9rY2+GZYKzzT3K/yFbVISM/F5PXncKjUczOiQ+WYOygM/m6av8UiIrJWzCbjU3uOktQO7k5Vf44Ss4moduNAiQxSUFSM11b9g92Xk+AiscOal59A8wCZQdtS5CgxYU2sWhCVePxMp3CdOh8REVkLZpPlYzYRES+9I4OIbW3w7fDWaB/sgcz8QoxaegK3HmUZtK3kLKXGIAKAg9eTkZzFyxyIiKh6MZuIiAMlMphUbIslo9qieYArUrKVGPHzCSSk5+q9nYy8Aq3zMyuZT0REZGzMJiLiQImqxFUqxoox7VHfywnx6bkY8fNxpGbr9ymbq1Ssdb5LJfOJiIiMjdlERBwoUZV5OkuwclwE/GVS3HyUjdHLTuj1SZvc2R7RoXKN86JD5ZA78xpwIiKqXswmIuJAiYwiwM0BK1+MgIeTPc7dV+ClX04hr6BIp3VljvaYOyisXCBFh8oxb1AYb5YlIqJqx2wiIna9I6M6f1+BYT8dQ1Z+Ibo18cGiF1rDzla38bgiR4nkLCUy8wrgIhVD7mzPICKiGonZZD2YTUS1FwdKZHTHbqVg5NITUBYWY2DrAHwxuCVsbETmLouIyGIwm4iILB8vvSOje6K+J74f3hq2NiJsOB2PT7deQg0fjxMRERFRDcOBEplEt6Y++GJIGABg2ZHbWLj3hpkrIiIiIiLSHQdKZDIDwutgWt+mAICvdl3DiqO3zVsQEREREZGO7MxdAFmekhtXM/IK4OoghtzJ8BtXx0QFQ5FbgAW7r2PanxchcxCjf3iAkSs2nDGPlYiITKc2na9r07ESWTIOlEhNQnouJq8/h0PXk1XTokPlmDsoDP5uDgZt862uoUjPKcDyo7fxzh9n4SK1Q9cmPsYq2WCmOFYiIjK+2nS+rk3HSmTpeOkdqShylOVOzgBw8HoyPlh/DoocpUHbFYlE+KRPUwwID0BRsYDxv57G8VspxijZYKY6ViIiMq7adL6uTcdKZA04UCKV5CxluZNziYPXk5GcZfgJ2sZGhPmDw9CtiTfyC4vx4opTuBCvMHh7VWXKYyUiIuOpTefr2nSsRNaAAyVSycgr0Do/s5L5lRHb2uDb4a3RPtgDmfmFGLX0BG49yqrSNg1l6mMlIiLjqE3n69p0rETWgAMlUnGVirXOd6lkvi6kYlssGdUWzQNckZKtxIifTyAhPbfK29VXdRwrERFVXW06X9emYyWyBhwokYrc2R7RoXKN86JD5ZA7G6fjjqtUjOVj2qO+3Anx6bkY8fNxpGZX7+UE1XWsRERUNbXpfF2bjpXIGnCgRCoyR3vMHRRW7iQdHSrHvEFhRm1NKneWYOWLEfCTSXHzUTZGLztRrZcUVOexEhGR4WrT+bo2HSuRNRAJgiDou9K9e/dw+/Zt5OTkwMvLC82aNYNEIjFFfVWWkZEBmUwGhUIBV1dXc5djFUqe35CZVwAXqRhyZ9M9v+FGUhaGLo5BarYST9T3wPIx7SEV25pkX5pU57ESEZVgNumvNp2va9OxElkynQdKd+7cwaJFi7BmzRrcu3cPpVezt7fHk08+iZdffhmDBg2CjY3lfFHFMLJ85+8rMOynY8jKL0S3Jj5Y9EJr2Nlazv8hIiJjYzYREVk+nd6NvvXWW2jRogWuX7+OmTNn4uLFi1AoFFAqlUhMTMS2bdvQsWNHfPzxxwgLC8PJkydNXTfVIC3qyPDTyLawt7PB7ssP8f76cygu1vuLTiIiIiIio9HpG6X33nsP77//Pry8vCrd4LZt25CTk4PBgwcbpcCq4qd21mPXpYd4ddU/KCoWMCYqCJ/0aQqRSGTusoiIjI7ZRERk+Qy6R8maMIysy4bT9zHp97MAgElPN8SbXUPNXBERkfExm4iILJ+dISsVFRUhOTkZIpEInp6esLWtvpvvyXqV3JyakVcAVwcx5E7lb04d2LoOFLkFmPHXJXy16xpkDmKMigyqdHsyBzGcJHbIyivUun1T1m5J2yUiIt0Y+zxs7dnEXCL6l14DpY0bN+KLL77AqVOnUFhY+HgDdnZo27Yt3nvvPfTv398UNVINkJCei8nrz+HQ9WTVtOhQOeYOCoO/m4PasmOigpGeU4Cv91zHtD8vQuYgRv/wgAq352hvi2+GhWPZkTgcuZFS6fZNWbslbJeIiHRj7POwtWcTc4lInc6txRYvXoznnnsOYWFhWLt2LQ4fPoxDhw5h7dq1CAsLw3PPPYeffvrJlLWSlVLkKMudeAHg4PVkfLD+HBQ55R82O7FbKEb//5ukd/44iz2XH1a4vbEdg8sFUWXbN2Xt5twuERHpxtjnYWvPJuYSUXk6f6P0+eef4/vvv8e4cePKzevfvz/atWuHzz77DC+99JJRCyTrl5ylLHfiLXHwejKSs5TlvtYXiUT4pE9TKHILsDE2HuN/PY1fxrZHRH3PctsLD3TDt3tv6LV9U9Zuzu0SEZFujH0etvZsYi4RlafzN0rx8fHo2LFjhfMjIyORkJBglKKoZsnIK9A6P7OC+TY2IswfHIaujb2RX1iMF1ecwoV4Rbnt5RcWG7R9XRhau7m2S0REujH2edjas4m5RFSezgOlZs2a4ccff6xw/k8//YRmzZoZpSiqWVylYq3zXbTMF9va4LvnW6N9sAcy8wsxaukJKHLVT9YSO+3/jbVtvzJVqd0c2yUiIt0Y+zxcdnvWlk3MJaLydL707ssvv0Tv3r2xfft2dO/eHT4+PhCJREhMTMSuXbtw584dbNu2zZS1kpWSO9sjOlSOgxq+0o8OlUPurP2rfKnYFktGtcWwH4/hYkIGpmw4j/ZB7jhxOw0AEHsvHVEhnuWuA9d1+0DFXX6qWntFTLVdIiLSjbHPw2W3Z23ZxFwiKk+v5yjdvn0bP/zwA44dO4bExEQAgK+vLzp06IBXX30VQUFBpqrTYHxWhWVISM/FB+vPqZ2Ao0PlmDcoDH46dtJJzsrH0EUxuJWcjXqejvCXSRFzK1XVWWj5kTgcLtNZSJftV9blxxi1V7RfU2yXiCwfs8kyGPs8XHp71phNzCUidXzgLFWbkk/GMvMK4CIVQ+6s/7MZ4tNzMfiHo3igyEMzf1fMGdACxYIA11LPqtBn+4ocJSasidV4A2t0qBwLh4VD5mhvlNor2r8ptktElo3ZZDmMfR4uvT1rzCbmEtG/DHrgLJEhZI5VP9kGuDlg5bgIDF0cg4sJGZjz9xUsG9MOUvHjhx776Pl+Q9cuP8aoXRNTbZeIiHRj7POwpu1ZUzYxl4j+pXMzh8qcPXsWtra2xtocUYVCvJ2xYkx7OEvsEHMrBW+siUVhkfbuQhVhlx8iIrI0zCYiy2C0gRIA1PCr+MiCtKgjw5JRbWFvZ4Ndlx5i8vrzKC7W//8fu/wQEZGlYTYRWQadL70bOHCg1vkKhQIikajKBZF1q6hDjzE8zMhDWrYSGXmFcHWwQ7DcCd8Nb41XV/6D9afvQ1lYhLe6hcLLWaLzPvXt8mPK4yMiItOozmxyd7SHj6u0SvtkNhFZBp2bOYjFYjz99NPw8fHROD81NRVbtmxBUVGRUQusKt4wW30q69BTFXdTsjFl43m1NqsdQzzx2YAWeHNNLM7eVxi8T127/Jjy+IiodmE2VR9zZdP87Vew9XyiwftkNhGZn84DpbCwMLz11lsYN26cxvlnzpxBmzZtOFCqpXTt0GOIhxl5mPT7GY3PougY4okxUcEYt+JUlfZZWZcfUx4fEdU+zKbqwWxiNhFVhc73KLVp0wanT5+ucL5EIkHdunWNUhRZH1069BgqLVupMYgA4PCNFHi7Sqq8T5mjPRp4O6NVXXc08HYuFyymPD4iIjINZhOziagqdL5HadGiRVq/LWrSpAni4uKMUhRZH1N26MnIK9Q6PytP8//LfVceooG3s8H7Va+BHYiIiKyNJWaTMfOC2URkWjp/oySRSODo6GjKWsiKmbJDj6tU+3jeWaq5Lf38HddwIi7V4P2q18AORERE1sYSs8mYecFsIjKtKrUH7927Nx48eGCsWsiKlXTo0URThx59uDvZo2OIp8Z5HUM8kZSRX266h5MYyqJijFt+EhfiFRrW1I8pj4+IiEzD0rLJ2HnBbCIyrSoNlA4ePIjc3Fxj1UJWTOZoj7mDwsqdsEs69FTlZlIfVylmD2hRLpCeDJHjswEtsPVcQrl9rn81Eu2DPZCZX4hRS0/g1qMsg/cPaD++2QNaICVbidi7abj5KAuKHF4TTkRkCSwtm6q6z7K0Hd/Mfs1xOyWbuURUBTp3vdPExcUFZ8+eRf369Y1Zk1Gxs1D1qqxDT1XcT83B3dQcpOcWQGJng9h76bj6IAPT+jaDsqgYGbnq+8zIK8CwH4/hYkIGAtwcsO61DvCTVa1Vatnjk4ptMO3Pi9h9OUm1DNuyElFlmE3Vy5KyyRRKH5+DvS1O303Hp1suIUf5+D4p5hKRYao0UGrevDn+/vtvBAYGGrMmo2IY1QyGtkBNzsrH0EUxuJWcjQZeTvjj1Uh4OBknqNiWlYgMxWyqGSwtByytHiJrV6VL7y5cuGDRgySqOQxtgSp3luCXce3hJ5Pi5qNsjF52Aln52jsVmbomIiKqGSwtByytHiJrp3N78BLXr1/H0aNHkZiYCJFIBB8fH0RGRiI0NNQU9REBqFoL1Drujlg5LgJDF8fg3H0FXlpxCsvGtINUrLkjUXXURERE1s/ScsDS6iGydjoPlBQKBUaOHIm//voLMpkM3t7eEAQBjx49QkZGBvr27YtffvmFlxCQSVS1BWqItzNWjGmPYT8dQ8ytFLy5JhbfP98adraGf6nKtqxERLWbpeWApdVDZO10fpf4xhtvIC4uDjExMUhLS8PVq1dx7do1pKWl4ejRo4iLi8Mbb7yh186DgoIgEonK/bz++usAAEEQMH36dPj7+8PBwQGdO3fGxYsX9TtCqhGM0QK1RR0ZfhrZFvZ2Nth56SE+2HAexcUG36LHtqxERLWcpeWApdVDZO10Hij9+eef+OmnnxAREVFuXkREBBYvXozNmzfrtfOTJ0/iwYMHqp9du3YBAIYMGQIAmD9/Pr766it8++23OHnyJHx9ffH0008jMzNTr/2Q9ctRFmF8lxBElW3Dqme71Q4NPPHtsHDY2oiw7p/7+GzbZRjaz8SUbWeJiMjyGSubjIW5RGRcet2jJBKJDJpXES8vL7W/z507Fw0aNECnTp0gCAIWLFiAqVOnYuDAgQCAFStWwMfHB6tXr8Yrr7yi9/7IOilylHh//Tn8cycNYzsGY2xUMPILiyGxs0FSZj4c7fW716h7M1/MHxSGd/44i58Px8HdUYwJTxl2j52/mwMWDgs3WdtZIiKyTMbOJmNhLhEZj84Dpb59++Kll17Czz//jLZt26rNO3XqFF599VU8++yzBheiVCqxatUqTJo0CSKRCLdu3UJiYiK6d++uWkYikaBTp044evRohQOl/Px85Of/+zTsjIwMg2siy1C6i8+3e2+Um98+yEPvABjUpg4UuQWYueUSvth5DTIHMUZ0CDKoPpkjA4iItGM21TymyCZjYS4RGYfOl94tXLgQ/v7+aN++PTw8PNC4cWM0adIEHh4eiIiIgJ+fH7755huDC9m0aRPS09MxevRoAEBiYiIAwMfHR205Hx8f1TxN5syZA5lMpvph+3LrZ6ouPmM7BuPNro+/Sfrkz4vYfCbeoO0QEVWG2VTzsMMcUc2n8zdKbm5u+Pvvv3HlyhXExMSoBiu+vr7o0KEDGjduXKVCfv75Z/Ts2RP+/v5q08te0icIgtbL/KZMmYJJkyap/p6RkcFAsnKm7OLzdrdQKHKUWBFzB+/8fhauUjG6NPY2eHtERJowm2oedpgjqvn0fo5S48aNqzwoKuvOnTvYvXs3NmzYoJrm6+sL4PE3S35+fqrpSUlJ5b5lKk0ikUAikRi1PjKvki4+Byt40nhVuviIRCJM69sMitwCbDqTgFdX/YOV4yLQPtijKiUTEalhNtU8pswmIrIMOl16FxMTo/MGs7Oz9W7hvWzZMnh7e6N3796qacHBwfD19VV1wgMe38d04MABREZG6rV9sm6m7uJjYyPC50NaIjpUjvzCYoxedgLbLzyAIodPMCciIs2qq8OcIkeJm0lZiL2bhpuPsphNRNVIp2+URo4ciaCgILz00kvo1asXnJ2dyy1z6dIlrFq1CsuWLcP8+fPRrFkznQooLi7GsmXLMGrUKNjZ/VuOSCTCxIkTMXv2bISGhiI0NBSzZ8+Go6Mjhg8fruPhUU1h6i4+jzLzUfj/ZyrlKIvw6qrTaFvPHd8MC4e/m4NR9kFERDWLqbMpIT0Xk9efUzWNAB4PxOYOCmM2EVUDkaDDQ2QKCgqwePFifPvtt7h58yYaNmwIf39/SKVSpKWl4cqVK8jOzsbAgQMxZcoUNG/eXOcCdu7ciR49euDq1ato2LCh2jxBEDBjxgwsXrwYaWlpiIiIwHfffafX9jMyMiCTyaBQKODq6qrzelR7KHKUmLAmVi2ISkQEuePHkW3ZPYiIjIrZRJXRlk3RoXIsHBbObCIyMZ0GSqWdPn0ahw4dwu3bt5Gbmwu5XI7w8HB06dIFHh6Wd18Hw4gqczMpC12/OlDh/A2vRaJ1PfdqrIiIajpmE1WmsmzaM6kTGniXv8KHiIxH72YOrVu3RuvWrU1RC5FZVNbi9f11Z7FpQkc4S/T+dSEiIjII248TmZ/Oz1Eiqqkqa/F641E2XlpxCnkFRdVUERER1XZsP05kfhwoUa1X0uJVk/BANzjZ2yLmVgreXBOLwqJivbZdW7oV1ZbjJCKqLtqyqSrtx2vT+bo2HSuZht73KFkbXgdOukhIz8UH68+pPQ+jpMXr7ZQcjFp2AsrCYgxqXQefDw6DjU3FDz0uvc3a0K2othwnkTExm0gX2rLJz4Dza206X9emYyXT4UCJ6P8UOcoKW7zuuvQQr676B0XFAsZGBePjPk0gElU8WKot3Ypqy3ESGVtVsylHWQhHe943WRtoyyZ9t1Nbzte16VjJtIxy6V16eroxNkNkVjJHezTwdkaruu5o4O2sdhJ9uqkPPh8cBgBYeiQO3+69oXVbyVlKjSdoADh4PRnJWTXj6//acpxEliQhPRfhM3fh1ZX/YMfFRCgL9bskmKyLtmzSR206X9emYyXT0nugNG/ePKxdu1b196FDh8LT0xMBAQE4e/asUYsjsiQDW9fBJ32aAgC+3HUNK2NuV7hsbelWVFuOk8iS7L/6CPmFxdh+MRGvrPwH7WfvxtSN5/HPnVTU8ItEqApq0/m6Nh0rmZbeA6XFixcjMDAQALBr1y7s2rULf//9N3r27In33nvP6AUSWZKxHYPxVtdQAMAnf17E5jPxGperLd2KastxElmS4RF1se3NJ/FydH34uEqQnlOAX4/fxaAfYtDp8/34atc1xCVnm7tMsjC16Xxdm46VTEvvgdKDBw9UA6UtW7Zg6NCh6N69O95//32cPHnS6AUSWZqJ3UIxqkM9CALwzu9nse9KUrllTNWtyNLUluMksjRN/V3xYa8mOPpBV6waF4GBrQPgaG+Lu6k5+GbPdXT5Yj/6f3cEK47eRkpWvrnLJQtQm87XtelYybT0bubg7++PdevWITIyEo0aNcKsWbMwZMgQXL16Fe3atUNGRoapajUImzmQJiU3x2bkFcDVQQy50+ObYyuaXlZxsYBJv5/BpjMJkNjZYOW4CLQP9ih1060SMkd7fLL5YrmOO2W7FRlSi651Vgdjd2Uiqg1MkU05ykLsuvQQG2Pjceh6MoqKH8e7nY0InRp6YUDrAHRr4gOp2NYo+yPjMuU5X5GjREq2EoIATP/LNLlkjDqNidlExqD3QGnChAnYsmULQkNDERsbi9u3b8PZ2Rlr167FvHnzcPr0aVPVahAOlKgsTS1Dn27ijY/7NMXUTRd0biVaUFSMV1f+gz1XkuAiscPC4eH4+XCcan1He1t81LsJWtSR4VFmPgLcHODrKlULjYral87q3xwzt1zC7stJatPnDQqDAFhcy1NjdWUiqi1MnU2PMvPx59kEbIqNx/l4hWq6i8QOPVv4YkB4HUQEe+j0qAMyvYqywBjn/NLbdrS3xdiOwehQ3xO2NiIIgoC6Ho4IcHestJaKcqmkDktsx81soqrSe6BUUFCAr7/+Gvfu3cPo0aMRHh4OAFiwYAGcnZ3x4osvmqRQQ3GgRKVV1DJ0wlMhiL2bhiM3Usqto62VaF5BEUYuPYETcakQ24pQUFT+1ykqxBPhdd1x7l662na0tS/tGOKJVnXdy3XXmzOwBbade4BDN9jylMiaVWc23UjKxMbYeGyKTUB8eq5qur9Min7hARgQHoCGPi4mrYEqpi0LqnrO17ZtTdlkSC5Fh8rx+ZCWePePs2zHTTWO3vcoicVivPvuu/j6669VgyQAmDhxosUNkojKqqhlaHigm8ZBEqC9lahUbIslo9oixNtZ4yAJAI7cSEF4oFu57WhrX3r4/+uU5e0i0RiYldVJRLVXiLcL3uvRGIfe74K1Lz+B59oFwkVqhwRFHn7YfxPd/3sQvb85hCWHbiEpI8/c5dY62rKgqud8bdvWlE2G5NLB68lIy2Y7bqqZ9B4orVixAlu3blX9/f3334ebmxsiIyNx584doxZHZGwVtQzNr+Q5JNpaibpKxZjaq7HW9Uu2X3o7lbUv1VRTVeokotrNxkaEiPqemDsoDCendsP3z7fG0019ILYV4WJCBmZtvYwn5uzByKUnsDH2PnKUheYuuVbQlgVVPefrmjMl2zEklx6vp/3/CrOJrJXeA6XZs2fDweHxtaYxMTH49ttvMX/+fMjlcrz99ttGL5DImCpqGSqx0/6rUFkr0boeTlrnl2y/9HYqa1+qqaaq1klEBDz+NrxXCz/8NLItTnzYDZ/2a4bWdd1QLAAHrz3C22vPou2s3Xh77RkcvPZI1RiCjE9bFlT1nK9rzpRsx5Bceryendb1mE1krfQeKN27dw8hISEAgE2bNmHw4MF4+eWXMWfOHBw6dMjoBRIZU0UtQ2PvpaNjiKfGdZ4MlaNIEHDzURYUOZovH9DWijQqxBOx99LLtSTVtk7H/69TVlJmPlueEpFRuTvZY0SHIGwYH4UD73XGxG6hCPJ0RI6yCBtj4zFy6Qk8MWcPZm25hAvxCj7U1si0ZYG2c/6ToXLY2YoqzKXKtq0pmwzJpehQOdyd2I6baia9B0rOzs5ISXl8L8fOnTvRrVs3AIBUKkVubq62VYnMTuZoj7mDwsqd0K8+yMDsAS3KTe8Y4olRkUHo/90RdP3yAN5YE4uE9PL/zyvaroeTGCM7BOHqgwzMGxSmdjNrRetEhXjik77NcDlBoTY9OlSOLg29NK5T0h2JN8sSUVXU83TCxG4Nse/dztgwPhIjnqgHd0cxHmXmY8nhOPRZeBg9FhzE9/tvaDwXkv4qygJt5/yo/2dTz68PVZhL2rYdFeKJMVHB5bKpsly6pCGX5g0Kg4+rlNlENZLeXe+ef/55XLlyBeHh4VizZg3u3r0LT09P/Pnnn/jwww9x4cIFU9VqEHa9I00qahlaMl2RW4C8giIcvZWCpYfjkKMsUq2rrYNPyfqHbyRj1tZLKCgS0KOZD+YObAF3J4nGWh5m5OFmUhbScwsgsbNB7L10/HbiLp5rXxeR9T0hFdtC5qDe1pQtT4msmzVlk7KwGAeuPcKm2HjsuvwQyv/fpyISARHBHhgYXgfPtPCt9LIt0k7beV2Ro0RSZj7upuYAeHwVROlsqqyzXOlsc7S3ha2NCLY2InhW8JwjTfsryaWShg51PRzh7SLR+BwlZhPVFHoPlNLT0/HRRx/h3r17eO211/DMM88AAKZNmwZ7e3tMnTrVJIUayprCiCzHzaQsdP3qQIXz90zqhAbezlq3sfNiIl779TSKigWM6xiMj3o3gUhU/pklxtgXEVkXa80mRW4B/j7/ABtj43E8LlU1XWJng25NfTCgVQA6NfKC2FbvC1aoEtWdFcwmIkD73XcauLm54dtvvy03fcaMGUYpiMgSVNb5R5cOPt2b+WL+oDC888dZ/Hw4Du6OYkx4KtQk+yIiqg4yBzGea18Xz7Wvi/tpOdh8JgEbY+NxIykLW889wNZzD+DhZI8+YX4YEB6AVoFuGj8gIv1Vd1Ywm4gMuEcJAA4dOoQXXngBkZGRiI+PBwCsXLkShw8fNmpxROZS2SUkunbwGdSmDj7p0xQA8MXOa1gZc9tk+yIiqk513B3xepcQ7Ho7Glve6IixUcGQO0uQmq3ELzF3MOD7o3jqywP4evd13E3JMXe5Vq+6s4LZRGTAQGn9+vXo0aMHHBwccPr0aeTn5wMAMjMzMXv2bKMXSGRKihwlbiZlIfZumlpXO22df3Tt4FOy7fC6bhjxRD0AwCd/XsTmM/FqyxljX8ZU0WtCRKSJSCRC8wAZPunbFMemPIXlY9qhXyt/SMU2iEvOxn93X0P05/sw6IejWHXsDtJ5TqmUpvOwsbJC13O8JWUTc4nMRe97lMLDw/H2229j5MiRcHFxwdmzZ1G/fn2cOXMGzzzzDBITE01Vq0Gs9TpwMr2E9FxMXn9O7Wni0aFyzB0UBn83BySk5+KD9edwsMz8eYPC4OfmoPe2/WVSJCjyYGcjwk8j26JLY2+15Q3dlzFV9poQkXHUhmzKyi/EjguJ2HQmHkduJKPkUUxiWxG6NPLGwNYB6NLYGxI7W/MWamEqOg/PGxQGAahSVuh7jreEbGIukTnpPVBydHTEpUuXEBQUpDZQunXrFpo2bYq8vDxT1WqQ2hBGpD9FjhIT1sSqnXhLlO4eZEgHH23b9naRICkzHxI7G6wcF4H2wR5q65mzW5CurwkRVV1ty6aHGXn480wCNsTG4/KDDNV0V6kdeof5Y0B4ANrWc4eNTe2+n0mX8zAAg7LC0HO8ObOJuUTmpnczBz8/P9y4cQNBQUFq0w8fPoz69esbqy4ik0rOUmo88QLAwevJSM5SQuZor/ox1raTMvPxRH0PHLuVinHLT2LNy0+geYAMAAzalzHp+poQEenLx1WKl6Lr46Xo+riSmIGNsfHYHJuAxIw8rDlxF2tO3EUddwf0bxWA/uEBCKml3dR0OQ838HY26Fxs6DnenNnEXCJz0/sepVdeeQVvvfUWjh8/DpFIhISEBPz666949913MX78eFPUSGR0puzmU9m23+4WivbBHsjML8SopSdw61GWwfsyJnY4IqLq0NjXFVN6NsGRD57C6hcjMKRNHThL7HA/LRff7ruBbl8dwLPfHsayI3FIzso3d7nVypzZZInneGusmWoWvb9Rev/996FQKNClSxfk5eUhOjoaEokE7777LiZMmGCKGomMzpTdfCrbttxZiiWj2mLYj8dwMSEDI34+gXWvdYCfzLzXWrPDERFVJ1sbESJD5IgMkWNmv+bYffkhNsbG48C1Rzh3X4Fz9xWYtfUyokPl6B8egO5NfeFgX7PvZzJnNlniOd4aa6aaxaD24J999hmSk5Nx4sQJHDt2DI8ePcKnn35q7NqITMaU3Xx02barVIwVY9ujvtwJ8em5GPHzCaRmm7eLjyV1OCKi2sXB3hZ9W/pj6eh2OP5hV0zv2xQt68hQVCxg39VHeOu3M2g7axfe+f0sjtxIRlGxXrdXWw1zZ5OlscaaqWbRu5mDtaltN8yS7kzZzUfXbd9Py8GQRTF4oMhDWB0ZVr/0BJwl/37R+zAjD2nZSmTkFcLVwQ7ujvbwcZVWuv+Sm28z8grg6iCG3Em3a8wtocMRUW3AbNLNzUdZ2BQbj42x8biflqua7usqRb9W/hjQOgCNfWvW62cJ2aSNIkeJ9JwCZCsLka0sgpuDGN4uEp0aHTGXyNroPVDKzs7G3LlzsWfPHiQlJaG4uFht/q1bt4xaYFUxjEgbU3bz0XXbN5KyMHRxDFKzlehQ3xPLxrSDVGyLuynZmLLxPI7cSFEt2zHEE7MHtEBdT6cK91vVVqrm7r5HVBswm/QjCAJO3UnDhtPx2HouARl5hap5TfxcMSDcH/1aBej0QZI1sIRs0uRBei7upOZg4d7ratn05P8HLhVlDHOJrJXeA6Vhw4bhwIEDGDFiBPz8/CASqbfyfOutt4xaYFUxjMganLufjmE/HkO2sgjdm/pg2rNN8f66c2pBVKJjiCe+HNpK4xsCtlIlsg7MJsPlFxZh35UkbDgdj31Xk1BQ9PhtjEgERDWQY0B4AJ5p7gsnid63YZMWihwltl1IxJZzCRqzqaKMYS6RNdP7LPL3339j69atiIqKMkU9RLVSWB03/DSqLUYvO4mdlx5CEKAxiADg8I0UpGUrNQ6U2EqViGo6iZ0tnmnuh2ea+yE9R4mt5x9g4+l4nLqThsM3knH4RjI+2nQB3Zv5YEB4ADqGyGFna9At2VRKcpYS3i6SCrOpooxhLpE103ug5O7uDg8Pj8oXJCK9RDaQ49th4Xjt19PYdfmh1mVLX3aiPp2tVImo9nBztMfzEfXwfEQ93E3JwaYzj+9nikvOxuYzCdh8JgFyZwmebemPga0D0MzftdyVMKSbjLwC5BcWa11GU8Ywl8ia6f0Ry6effopPPvkEOTk5pqiHqFbr3swX8weFVbqcq1TzZxxspUpEtVVdT0e82TUUe9/phE2vR2F0ZBA8nOyRnJWPpUfi0GfhYTz934P4bt8N3E/jexh9uUrFkNhpf9uoKWOYS2TN9P5G6csvv8TNmzfh4+ODoKAgiMXq/8FPnz5ttOKIzM2QLj2GdvYpMahNHdxPy8F/d1/XOL9jiCfcnTRvr6SV6kENlzk8GSpHkSDg5qMsvWsiIrIWIpEIrQLd0CrQDVN7N8HBa4+wMTYeuy49xI2kLHy+4yo+33EVEcEeGBAegJ4t/CBzsJ4364ZmTFWzSe5sjxO3UxEV4lnhPUqa2nVry6XoUDnsbESIvZtmUE1EpqZ3M4cZM2ZonT9t2rQqFWRsvGGWDGVIl56qdvYpbfrmC1gec0dtmq5d78q2Uu0Y4onRUcF4c00scpRFBtdERMbBbKp+GXkF2H4+ERtj43EsLgUl737s7WzQrYk3+rcKQOdG3rCv5FsTczI0Y4yVTdq63s3X0q5bUy49GSrH611CMHb5SeQoiwyuiciU+BwlIg0M6dJj7M4+giDg/XXn8Mc/92FrI8InfZrimea+ej1HSZFbgLyCIhy9lYKlh+NUYWRoTURkHMwm80pIz8XmMwnYGHsf1x5mqaa7O4rRJ8wf/cMD0Lqum0Xdz2Roxhg7m0o/RylHWQSZns9RyswrgJPEDqfupOHTLZfUcsnQmohMhb0ziTQwpEuPsTv7iEQizBsUBmVRMTafScDsbZfRxM9Vp4GSzPHx5Qs3k7Iw8IejRquJiKgm8HdzwGudG+DVTvVx6UEGNp6Ox+azCXiUmY+Vx+5g5bE7qOfpiP6tAjAgPABB8oq/xa8uhmaMsbOpJF/0VXq9m0lZmLLhvNFqIjIVnQZKHh4euHbtGuRyOdzd3bV+wpKammq04ojMxZAuPabo7GNjI8IXQ1oiM68Qe68kYdzyk1jz8hNoHiDTaX12GyIiqphIJEIzfxma+cswpVcTHLmRjE2x8dh+MRF3UnLw9Z7r+HrPdbSu64YB4QHoE+Zf4T2ipmbo+dwSc8ASayLSRKeB0n//+1+4uLgAABYsWGDKeogsgiFdekzV2Udsa4Pvn2+NkT+fwInbqRi19AT+eLUD6ns5V7ouuw0REenG1kaE6IZeiG7ohVnKQuy8+BAbYuNx+PojnL6bjtN30zFzyyV0auiNga0D8FRjb0jFttVWn6Hnc0vMAUusiUgTnQZKo0aN0vhnopqqsi49hnT20bSOrqRiWywZ3RbPLT6GSw8yMOLnE1j3Wgf4ybTf8GrKmoiIaipHezv0Dw9A//AAJGXk4c+zCdgYG4+LCRnYffkhdl9+CBepHXq38EP/8AC0D/KAjY1p72cy9HxuiTlgiTURaWJQM4eioiJs3LgRly9fhkgkQpMmTdCvXz/Y2VneLU+8YZYMpalLT3SoHPP07OxT2TqV+bcxgxLKomK8+8c53E/LRX25E9a9FgmPSi4DMUVN2uo0tPUsUW3CbLJO1x5mYmNsPDbHxiNBkaeaHuDmgP7h/hgQHoAQbxeT7d/Q87mxc6D0+d5ZYgd7Wxuk5yrhLNX93M9sImug90DpwoUL6NevHxITE9GoUSMAwLVr1+Dl5YU///wTLVq0MEmhhmIYUVWU7tLjIhVD7qz7c5T0Wacimlq6tqnnhluPspGWU4DGvi5Y91oknCXaP6QwZk261sk2r0QVYzZZt+JiAcfjUrEx9j7+Pp+IzPxC1bwWATL0Dw/Asy394eUiMfq+DT2fGysHNJ3vo0I8Meb/j6BoW89d53M/s4ksnd4DpSeeeALe3t5YsWIF3N3dAQBpaWkYPXo0kpKSEBMTY5JCDcUwImulraVreF0ZLj/IRF5BMdoFuWPluIhqvVa+NGO3niWqDZhNNUdeQRF2X36ITbHx2H/1EQqLH7+tsrURoWOIHANbB+Dppj5wtLe8q270pe18HxXiifC67vh27w2LOPczm8gY9P6tPXv2LE6dOqUaJAGAu7s7PvvsM7Rr186oxRHVZtpausbeVeDj3k3w6dbLOHk7DW+uicX3z7eGnW31PyjR2K1niYisiVRsiz5h/ugT5o+UrHxsPf8AG07H48y9dBy49ggHrj2Ck70tejT3xcDwOujQwBO2Jr6fyVS0ne+P3EjB2KhgAJZx7mc2kTHoPVBq1KgRHj58iGbNmqlNT0pKQkhIiNEKI6rtKmufWnINt9hWhJ2XHuKDDecxf1CYyW8oLottXomIHvN0lmBkhyCM7BCEuORsbIyNx6bYeNxNzcGG0/HYcDoePq4S9Pv/85ma+FnXt4mVne/zC4tVfzb3uZ/ZRMag98fPs2fPxptvvol169bh/v37uH//PtatW4eJEydi3rx5yMjIUP0QkeEqa58qsXv86/tx76awtRFh3T/38dm2yzCgP0uVsM0rEVF5wXInTHq6IQ681xnrX+uA5yPqQuYgxsOMfPx48BZ6fn0Izyw4iMUHbiKxVGMIS6ZrLgHmP/czm8gY9P5GqU+fPgCAoUOHqh48W/LGrG/fvqq/i0QiFBUVGatOIpOylK44pevwcKq4fWpUiCdi76XjyVA5ejT3RZEgYMZfl/Dz4TgUFhdjfOcQ+LhKq6VmtnklIqqYSCRCm3oeaFPPA5/0bYr9Vx9h4+l47L2ShCuJmZjz9xXM3X4FkQ080b9VAHq28FM16LG0bCoSBDwZKq/wHqXYe+kAgCdD5XCW2iEpIw+p2Upk5BXC1cEO7o72zCayKno3czhw4IDOy3bq1EnvgoyNN8xSZSylK07ZOhztbbF0dDt8t++Gxu5Cq4/fwfCIevjtxF2M6xiMkUtPoKDo8a9zAy8nLBvdDnU9naqt9upo80pUUzCbSJFTgK3nH2BTbDxO3E5VTZeKbdC9qS+iG8qxKTYeh2+kqOaZO5sc7W3xzbBwLD8Sp1ZX6a534XXdMCYqWJVN41acQo7y8QfnHUM8MXtAC2YTWQ2DnqNkTRhGpI2ldMWpqA5He1t81LsJGvm6oKBIgIvEDsqiYihyCnDqbhqWHo5DjrJIrdtQiUY+zvhlXES1fXpn6javRDUJs4lKu5eag81n4rEhNh63HmVrXdbc2eRob4uxHYPRob4nigUBbo5i2IpEuJeWCzsbEWLvpWvNpo4hnvhyaCtmE1kFg3pV5uXl4dy5c0hKSkJxcbHavGeffdYohRFVB0vpilNRHTnKIny48QJ+HtUWAPDcj8c0rl+621CJqw+zsONCIkZGBhm9Xk1kjgwfIiJDBHo4YsJToXi9SwjO3Vdg+dHb2Bgbr3FZc2dTjrII3+69gW/33sDPo9oiKSMf41ac0ri+pmw6fCMFadnKahsoMZuoKvQeKG3fvh0jR45EcnL5N3W8L4msjaV0xdGnk5A+y3y69RKa+LuiXZCHwbUREVH1EIlEaBnohpEd6lU4UAKAN9bE4oUn6qF3Cz/IHE3XlMBU2ZSRV6hhSSLLo3fXuwkTJmDIkCF48OABiouL1X44SCJrYyldcXTpJFS6m1BFy5RVUCRg7PKTuJigqFJ9RERUfSrLhEsPMvDhxvNo99luvLryH+y4mIj8QuO/BzNVNrlKrf/hu1Q76D1QSkpKwqRJk+Dj42OKeoiqVUlXHE2qsyuOtjpKOgnF3ktHVIin1mVK61DfAy3ryJCZV4hRS08gLln7de9ERGQZtGVCRJA73u4Wisa+LlAWFWP7xUS8svIftP9sD6ZuPI9Tt1ON9pgIU2RTxxBPuDvxUjiyDnoPlAYPHoz9+/cbrYD4+Hi88MIL8PT0hKOjI1q1aoV//vlHNV8QBEyfPh3+/v5wcHBA586dcfHiRaPtn2oXRY4SN5OyEHs3DTcfZQEA5g4KKxcEJV1xquu6ZpmjvcY6ngyV442nQrH0cByWHo7DmKhgdCwTSE+GyvFGl8fLqKaFeGLeoDCsfDECTf1ckZylxAtLjuOBIrfca6DIUVZYlz7LavIwIw9XHmTgRFwqriRm4GGGdTwrhIioOumTTQueC8db3Rpi+8Ro/P3Wk3g5uj58XCVQ5Bbg1+N3MXhRDKI/34evdl7Frf9vy1AVZVPH/3e5K8mmN54KxZOa8ktDNs0e0KLc/Un6Zk1VskmRo8Sd5GxcSlDg5O1UXH+YqXe2Ue2hd9e7nJwcDBkyBF5eXmjRogXEYvWvZd98802dt5WWlobw8HB06dIFr732Gry9vXHz5k0EBQWhQYMGAIB58+bhs88+w/Lly9GwYUPMmjULBw8exNWrV+Hi4lLpPthZiEpU1AZ83qAwONrbWkRXHE3deQCoprk6iOEksUNWXiEy8wrgYG8LQQDO30+Hh7ME+YXFkNjZICkzH50besHPzQGPMvMxdHEM4pKzEeTpCF+ZFMdu/duKtqJ2s1Vtm343JRtTNp7HkVItZKu7NSyRpWI2UQljZFNRsYCYmynYEHsf2y8kqtpxA0DLQDcMDA9AnzA/eDpLDKqxbDY5S+2QnV+IjFzNWaVLNlV2/BVlTVWy6UF6Lu6k5mDh3utq2fTk/1/v6my7TtZB74HSkiVL8Oqrr8LBwQGenp6qh84Cj29CvHXrls7b+uCDD3DkyBEcOnRI43xBEODv74+JEydi8uTJAID8/Hz4+Phg3rx5eOWVVyrdB8OIAMtpA25Mihwltl1IxJZzCWon/BKlj+t+Wg4Gfn8USZn5GrdV9jWo6uv1MCMPk34/o7Gu6m4NS2SJmE0EmCabcpSF2HXpITbGxuPQ9WQUFT9+m2dnI0Knhl4Y0DoA3Zr4QCq2NcoxlKVPNul7/FV5vfSpi6iE3pfeffTRR5g5cyYUCgVu376NuLg41Y8+gyQA+PPPP9G2bVsMGTIE3t7eCA8Px08//aSaHxcXh8TERHTv3l01TSKRoFOnTjh69KjGbebn5yMjI0Pth0iXNuDWJjlLCW8XicYTPqB+XHXcHTF3YIsKt1X2Najq65WWraywrpLWsES1CbOJNDFFNjna26FfqwAsH9Mex6Z0xSd9mqJFgAyFxQL2XEnChNWxaDdrN9774yyO3kxGcbFxH6epTzbpe/xVeb30qYuohN4DJaVSif/85z+wsdF71XJu3bqFH374AaGhodixYwdeffVVvPnmm/jll18AAImJiQBQrnGEj4+Pal5Zc+bMgUwmU/0EBgZWuU6yfpbSBtyYMvIKKm3NWvq4Krt5tvSyVX29Kmv9ytawVNswm0gTU2eTl4sEYzsG4683OmL3pGi83qUBAtwckJlfiD/+uY/hPx1H1Ly9mPv3FVx7mFmlfZXQJ5v0Pf6qvF76ZiYRYMBAadSoUVi7dq1Rdl5cXIzWrVtj9uzZCA8PxyuvvIKXXnoJP/zwg9pypS/vAx5fkld2WokpU6ZAoVCofu7du2eUWsm6WUobcGNylYorbcta+rj0eQ2q+npV1vqVrWGptmE2kSbVmU0h3i54r0djHHq/C9a+/ASeaxcIF6kdHijysOjATXT/70H0+voQlhy6haQqNN7RJ5v0Pf6qvF76ZiYRYMADZ4uKijB//nzs2LEDYWFh5Zo5fPXVVzpvy8/PD02bNlWb1qRJE6xfvx4A4OvrC+DxN0t+fn6qZZKSkipsTy6RSCCRGHazItVcJS1OD1ZwXXN1tQEvreTm2Iz/N2mQO+nXQELubI8Tt1MRFeJZ7lICR3tbzHi2GWxtgEsJCmQriyBzEOPzwWGY9udFtRt97WxEmNmvGYoFAbF30+Dq8PhG3Yper6ebeMNZaoebSVkV1u7uZI+OIZ44XME9SsZuDVvV15LI1JhNpIk5ssnGRoSI+p6IqO+J6c82w94rSdhwOh77rybh0oMMXNqagdnbLqNlHTf0aO6Lfi391ZovVKaibHK0t8XYjsHo2MATRcXFuJKYgez8Qvz20hM4fDMZSw/HqWWTpqypSjZpy0zANK83s8n66T1QOn/+PMLDwwEAFy5cUJtX0bc8FYmKisLVq1fVpl27dg316tUDAAQHB8PX1xe7du1S7VOpVOLAgQOYN2+evqVTLVbS4vSD9efUTrDV3Qa8RFU7ygGPj6lzQy8Eyx93kCs58Tva22L5mHYoLgambrpQrrPPstHtMGb5SVUgFRYLWHzgFj7c+O/v89NNvDGrf3N8tOmC2uv1dBNvfNynKd7946zW2n1cpZg9oAU+3HhebbDUsYLWsFVhjNeSiMgczJ1NUrEterXwQ68WfkjNVuLX43fw48FbyMwrVD0jaf72K3i6qQ+ej6iHqBA5bG20v9fTlE2O9rb4Zlg4Vh+/g1aBbli471K5jqjfDAvHm2tikaMsqjBrqpJNFWUm8G/XO2O+3symmkHvrnfGdPLkSURGRmLGjBkYOnQoTpw4gZdeegk//vgjnn/+eQCP24PPmTMHy5YtQ2hoKGbPno39+/ezPTgZRFP77eoeJBm7y5EiR4n0nAJkKwuRoyyC3NkeMTdTseW85s4+T4bKMatfc6TlKLH78kN8u++mxu0+3cQbswa0ULUiL2kJWzaItNX+MCMPadlKZOQVwlVqB3cne6MOkmpiN0OqHZhNVJqlZ1MJLxcJ+rX0R//wADTzd9X6AXnpbLK1EWHWlktoWdcdsXfTKsymT/o0hY1IpDVrqppNZTNT5iCGt4vEqK83s6nmMOuNAu3atcPGjRsxZcoUzJw5E8HBwViwYIFqkAQA77//PnJzczF+/HikpaUhIiICO3fu1GmQRFSWzNH8X3vr0rVHnxrLHtPNpCx4u1bc2efQ9WQUFgtoVdcdLlJxhQOlXZeT8EHPQjTwdlbbtj61+7hKTdoG3NivJRGROVh6NgGAi9QOjzLzseRwHJYcjkNDH2cMCK+Dfq38NX5DUvqYbiZl4dCNFIyOCsa3e29o3P6h68mwEYnQwNtZa9ZUNZuq47VmNtUceg+UunTpovUThL179+q1vT59+qBPnz4VzheJRJg+fTqmT5+u13aJLJWpuxxZaschU7C0eoiIrFVl59Olo9oiPbcQG2PvY/flJFx7mIV5269g/o4reCLYEwPCA9Czha/Ghggl22Y2PcZssh56D5RatWql9veCggKcOXMGFy5cwKhRo4xVF1GNZeouR65SMVIreU6ROToOmYKl1UNEZK0qO596OEnQLtgTTzf1gSK3ANsvPMCG0/E4HpeKmFspiLmVgo83X0C3pj4YGB6A6IZeENvaqG3bErvhmYKl1UOG03ug9N///lfj9OnTpyMrK6vKBRHVdKbucqRLZ5+SzkCKXCXWvBSBIzdT8POhW8gt+PfTvtZ13crVYgndA0t3EXKW2GHOwBb4dMsltW5J1VkPEVFNoM/5XeYgxn/a1cV/2tXF/bQcbD6TgI2x8biRlIWt5x5g67kH8HCyR98wP/QPD0CwpyOiQ+WIvZeudzaV7Yan6dzObCJTMVozhxs3bqB9+/ZITU01xuaMhjfMkiVKSM+tsMuRPm1YK/IgPRd3UnOwcO/1cp19PuvfHDO3XMLuy0mq6R1DPDE6KhhvrD6tGix5ONlj/WuRqg5B1VW7Npq6CD0ZKsfrXUIwtlQnv+qqh8hQzCayRFU5vwuCgIsJGdhwOh5/nk1Acla+al6w3AldG3vj8oMMjOkYjGVH4tSyKTpUjllasqmkG562WphNZApGGyitXLkSkydPRkJCgjE2ZzQMI7JUpu5ypKmzj6uDGFM3nlcLohIlHYdylEV4f/05XE3MRICbA9a/FglfmbTctqu7Q1NlXYQ+/X8nP3N1jCLSB7OJLJUxzu+FRcU4fCMZG2PjseNiIvJKXa3Q3N8VTzX2xpOhctja2ED2/+cjVZZNNiJRpbUwm8jY9L70buDAgWp/FwQBDx48wKlTp/Dxxx8brTCims7UnXc0bf9mUpbGIAL+7TjUMtANq8ZFYOjiGMQlZ+OFn4/j91c6wKPUQ2LN0aGpsi5CJZ38iIjIcMY4v9vZ2qBzI290buSNrPxC7LiQiE1n4nHkRjIuJGTgQkIGfjhwE10aeWNg6wDU83CqNJtKd7kzZe36YjbVbHoPlGQymdrfbWxs0KhRI8ycORPdu3c3WmFEZHy6duLxcpFg5bj2GPxDDG4kZWHMshP49aUn4Cwx3xMF2EWIiMj6OEvsMKhNHQxqUwcPM/Lw55kEbIiNx+UHGdh56SF2XnoIJ4mt1m1Y8vmd2VSz6fWup6ioCKNHj0aLFi3g4eFhqpqIyET06cRTx90Rq15sjyGLYnD2vgIv/3IKS0e3g1SsPdBMhV2EiIism4+rFC9F18dL0fVxJTEDG2PjsTk2AYkZeVrXs+TzO7OpZtPep7EMW1tb9OjRAwqFwlT1EJEJlXQG0kRTJ54QbxcsH9MeTva2OHozBW+uiUVhkfbnYJiKvrUTEZHlauzriik9m+DIB0/hxxFt4OMi0bhcAy8naHl8p9kxm2o2vZs5tGvXDnPnzkXXrl1NVZNR8YZZosdKbnJNy1GioKhYre1qZZ14dl1MxOurT0NZJKBzIy9M6dkYvq5Sk1wLXrrFqquDGHKnf685N2dXIyJjYjYRPVY6m7LyC7D6xD3svvQQxaXendraiBAdKseA1nXwdBMfONjbqtYtaVqUrSyCm4MY3i4SZhMZjd4DpZ07d2Ly5Mn49NNP0aZNGzg5qbcOtrQTPsOIqOLWpdOfbQYRAE+nim+ALWk1Pv3Pi7iSmKma3jHEE/MHt4S/EUNAU53RoXLMHRSm2o85uhoRGRuziUh7NqXnKHH8Vip2XEzE2fv/XsnkLLHDM8190SlUDk9nCb7bf6PcYzDmlcoMU9XJbKod9B4o2dj8e7WeqNR3oYIgQCQSoaioSNNqZsMwotqustalC4eFV3gyV+Qose1CIracS6jwAYHa1q+uOomsDbOJajt9zvk3krKw+Uw8NsbG435armo5e1sbKDVcDs5sImPRu4XVvn37TFEHEZlIZa1Lk7OUFZ7kk7OU8HaRaBwk6bJ+ddVJRETWRZ9zfoi3M97p3ghvd2uIU3fSsOLobey7mqR6kGtl61dXnVTz6D1Q6tSpkynqICITqUrr0oy8AuQXam/esPV8At7s2tCg2sruSxu2WCUiqjkMOefb2IjQPtgDYlsRujfzwVu/nalw/T/PxOPlTg3gVMXHWjCbaje9//ecO3dO43SRSASpVIq6detCItHcuYSIql9VWpe6SsVIzVZqXf/rPTfQIsANXRp7G1Rf6X1pwxarREQ1R1WzqbLn+n299wZ+PBSH7s18MCA8AB1D5LCz1avZc5XrJOun90CpVatWavcmlSUWi/Gf//wHixcvhlQqrVJxRFR1Ja1LD1ZwfXVFrUsVOUrY2YiQlJmPqBBPjZffeblI8CgzH6/9+g9WjotAuyAPrZ2BTFEnERFZH0PO+SX5UiQIWrOpnocjBAB3U3Ow+UwCNp9JgKeTPTo38kKH+p5oVdcNXs66dcdjNtVuejdz2Lx5MyZPnoz33nsP7du3hyAIOHnyJL788ktMmzYNhYWF+OCDD/Cf//wHX3zxhanq1hlvmCXSv3VpSYeff+6k4bvhrSEV2+DbfeU7C83u3xzT/rqEvVeS4CK1w7fDwrHkcJzWzkDGrJPIWjGbiPQ755fuPOdob6s1m+YPCoOvTIqz9xXYePo+Np9JQHqu+iVyQZ6O+HJoS7Sp52HUOqlm0Xug1L59e3z66afo0aOH2vQdO3bg448/xokTJ7Bp0ya88847uHnzplGLNQTDiOgxXVuXlu3w42hvi1c61UeXRt4QiYC8gmK1Z1XkKoswculxnLydBrGtCAVF5U8p+nQGYotVqg2YTUSP6XLO19R5riSbnm7iAwDIURZBpuE5SoocJcavPl1hU6K29dwxuE0d9GzhB5lDxZfRMZtqJ70HSg4ODoiNjUXjxo3Vpl+5cgXh4eHIzc3F7du30bRpU+Tk5Bi1WEMwjIj0czMpC12/OlDh/D2TOqGBt7PaNEVuAQZ9fxQ3HmXptR5RbcVsItKdIbmk67ol7O1s0K2JNwaE10Gnhl6wt9P/fiaqefT+X9C4cWPMnTsXSuW/N3gXFBRg7ty5qsFTfHw8fHx8jFclEVUbQzr8yBzEmNq7sYalta9HRERUmap2b9Vm5BN10dDHGcrCYmw7n4iXfjmFiNm78fGmC/jnThr0/D6Bahi9mzl89913ePbZZ1GnTh2EhYVBJBLh3LlzKCoqwpYtWwAAt27dwvjx441eLBGZnqEdfup6OBm0HhERkTZV7ZCnzajIYMzo1xwXEzKwKTYem88m4FFmPlYeu4OVx+6gnqcj+rcKwIDwAATJtecc1Tx6D5QiIyNx+/ZtrFq1CteuXYMgCBg8eDCGDx8OFxcXAMCIESOMXigRVQ9DO/ywMxAREZlCVfJFl3VFIhGaB8jQPECGD3o2xtGbKdgYG4/tFxJxJyUHX++5jq/3XEd4XTcMDA9A7zB/eDgx02oDve9Rsja8DpysmaGttquqog4/8weFwcHetsKaNK3n6WSPTa9HIrCSb5yIahNmE1kzc2RTZZ3ntNVkaNe67PxC7LyUiA2n43HkRjKK//+O2c5GhM6NvDGwdQCeauwNqdjWNAdNZseBEpGFKt0KtYQ+rbarSlOHn2xlUaU1lax39GYyZm65hIIiAYPb1MH8QWGwsan4GWxEtQmziayVObOpos5zutRU1a51SRl5+PNsAjbGxuNiQoZquovUDr1b+GFAeADaBXkw52oYDpSILJCmVqgl9Gm1be6adlxMxGur/kGxALzYMRhTezfR+sBqotqC2UTWqKZkU1Vde5iJjbHx2BwbjwRFnmp6gJsD+of7Y0B4AEK8XYy6TzIP9j4kskDJWUqNJ30AOHg9GclZSo3zTMmQmno088W8QWEAgCWH4/D9fvM/W42IiAxTU7Kpqhr6uGDyM41xePJTWPPSExjatg6cJXaIT8/Fd/tuottXB9F34WEsPRyHR5n5Rt8/VR+9mzkQkelVpRWqqRha05C2gcjIK8SnWy7h8x1X4eogxogn6pmiRCIiMqGalE3GYGMjQocGnujQwBMz+zXH7ssPsSk2HvuvPsL5eAXOxyvw2bbLeDJUjgHhAeje1BcO9ryfyZroPVAaPXo0xo4di+joaFPUQ0SoWitUU6lKTeM6BkORo8Q3e2/gk80XIHMQ49mW/sYukYiITKimZZMxScW26BPmjz5h/kjJyseWcw+wMTYeZ+6lY//VR9h/9RGc7G3Ro7kvBobXQYcGnrDl/UwWT++BUmZmJrp3747AwECMGTMGo0aNQkBAgClqI6q1LLHVdlVrevvphkjPLcAvMXfw9tozSMrIQ5fG3uW6JenaTcnYXZfM1WGQiMha1MRsAio//+uTD4ocJdJzChBWR4aOoXJk5RVgz5VH2BQbj7upOdhwOh4bTsfDx1WCfq0C0L9VAJr6V3yfIrPJvAxq5pCSkoJVq1Zh+fLluHDhArp164Zx48ahX79+EIst66GSvGGWrJWh7Uwtuab7aTkY8P1RtWu2S3cm0rWbkrG7Lpm7wyDVPswmslY1LZsqO//rkw/alvWTSfHPnTRsjI3HlnMPoMj995LAxr4u6B8egH6t/OEnM13Wkf6q3PUuNjYWS5cuxZIlS+Ds7IwXXngB48ePR2hoqLFqrBKGEVmzqrYztaSaKutM9PmQlnj3j7OVdi4ydocjS+ziRDUfs4msWU3JJmPlki7bKr1sfmER9l99hI2n47H3ShKURcUAAJEI6FDfEwPCAxDZwBMfbDjPbDKzKjVzePDgAXbu3ImdO3fC1tYWvXr1wsWLF9G0aVPMnz8fb7/9trHqJKqVZI7mD5+yDK2pss5EadmVdy6SOdrr1OFIn/qMvT0iopqupmSTsXJJl22VXlZiZ4sezXzRo5kvFDkF2Hr+ATbG3sfJ22k4ejMFR2+mQGJng/zCYp22R6aj90CpoKAAf/75J5YtW4adO3ciLCwMb7/9Np5//nm4uDzuGf/bb7/htdde40CJiFQq60x0OyVb6/ySzkXG7nBkiV2ciIjI9Co7/2fkFWqdXzofDM0SmaMYwyPqYnhEXdxLzcGm2HhsjI3HrWTdMpFMS++Bkp+fH4qLizFs2DCcOHECrVq1KrdMjx494ObmZoTyiKimqKwz0extV7TOL+lcZOwOR5bSMYmIiKpXZed/V6n2t8ml88EYWRLo4Yg3uoZiwlMh2Hb+AV5fHVvhstn5RZVuj6pO7wfO/ve//0VCQgK+++47jYMkAHB3d0dcXFxVayOiGqSkM5Em9nY2uJuaAxeJ5lAq3blI23YM6bpk7O0REZF1qOz87+6kez4YM0tEIhE6hsgr3B4APP/zcQxdFIPVx+9CkcNvl0ylys0cLB1vmCVrYY4WoKX36Syxg72tDdJzlXCWGr7/hxl5SMtWIiOvEK4OdnB3tIePqxRAxZ2JXuvUAONXn0ZaTgHcHMRIL9UN6MlQOT7t1xzujmJVPcbuumSJXZyoZmM2kbWoCdlU0rI7W1mIbGUR3BzE8HaRQOZoX+n5X9N8TbkEVE82NfRxhqtUjH/upqHkHby9rQ26NvFG//AAdGnkDXs7vb8HoQroPFAaO3asThtcunRplQoyNoYRWQNztADVtM+oEE+MiQrGm2ti0baeu977v5uSjSkbz+PIjRTVtI4hnpg9oAXqejoBqLgz0dl76Rj+0zFkK4sQFeKJUR2CYGsjQuy9dCw9HFeuHmN3XbLELk5UczGbyBrUhGx6kJ6LO6k5WLj3ulo2Pfn/AYy/m0Ol539FjhKJGXm4n5YLABXmUsmy1ZFNDxS52HwmARtPx+Pqw0zV8m6OYvQJ88OA8AC0rusOkYgPta0KnQdKNjY2qFevHsLDw6FtlY0bNxqtOGNgGJGlM0d7am37jArxRHhdd3y794Ze+3+YkYdJv59RC6ISHUM88eXQVqpvlipy9GYyRi89qWqVWhZbolJNwWwiS1cTskmRo8S2C4nYci5BYzbpsx1LfozEpYQMbDoTj02x8Ugq9ZzCuh6O6B8egAHhAQiWO5mtPmumczOHV199Fb/99htu3bqFsWPH4oUXXoCHh4cpayOqFczRnlrbPo/cSMHYqGC995+WrdQYRABw+EYK0rKVlQ6UIhvIMbV3E0z786LG+WyJSkRUPWpCNiVnKeHtIqkwm/TZjiU/RqKpvyua+rti8jONEXMzBRti72P7hUTcTc3BN3uu45s919Eq0A0DWwegT5g/PJyYobrS+SLG77//Hg8ePMDkyZPx119/ITAwEEOHDsWOHTu0fsNERNqZoz11Zfss/ewGXfdfWRvVyuaXCKsj0zqfLVGJiEyvJmRTRl5Bhc8i0nc7Vd1GdbC1EaFjqBxfDW2FUx91w9fPtUKnhl6wEQFn7qXjk80X0f6z3XhxxUlsPfcAeQXsnFcZvdqDSyQSDBs2DMOGDcOdO3ewfPlyjB8/HgUFBbh06RKcnZ1NVSdRjWWO9tSV7VNS6kZQXfdfWRvVyub/uxzbdRMRmVtNyCZXqRip2Uqty+i6napuo7o52tuhX6sA9GsVgKTMPPx19vFDbS/EZ2D35STsvpwEF4kderbwxYDwOogI9oCNDe9nKkvv5yiVEIlEEIlEEAQBxcXaR+tEVLGSlqIHK7j22RTtqbXtMyrEE7H30vXev7uTPTqGeOJwBfcouevwVb8iR4liQcDPo9pCJBLh+/03cOp2mmq+rvUocpRIyVaisFhAsSAgJ78QMkf7aunWRERUE9SEbJI72+PE7VREhXhWeI+SLttxltph9YsRSM8tgFRsi9N307D0cBxylEV65VJylhJZ+QVwc7SHsrAYWfmF1dJJ0NtFinEdgzGuYzCuP8zExth4bD6TgPj0XPx+6j5+P3Uf/jIp+oUHYGB4AEJ9XExWi7XRqz14fn4+NmzYgKVLl+Lw4cPo06cPxowZg2eeeQY2NpbZipA3zJI1MEd7ak37LNtZSN/9303Jxocbz6sNlsp2vdNWT9lOR5ENPCCxs8W+q48gAvDl0JYY2LpOpdv5ZPMFPNe+LpYdiVMLR1N3ayLSFbOJrEFNyCZtXe/m67AdbV341p64i5n9muu8jX/upOGbYeEWkU3FxQJO3E7FxtPx2Hb+ATLz/708vpm/KwaEB+DZVv7wdtF+b3FNp/NAafz48fjtt99Qt25djBkzBi+88AI8PT1NXV+VMYzIWpijPXXpfTr9/1kVilwlnCSG71/tOUpSO7g72VfaxEFbR6GOIZ6Q2Nliz5UkSMU2WDUuAm2DNDeSKdlOy0A3xN5Nq1KXIyJTYjaRtagJ2VT6OUo5yiLISj1HqbL1KsqmJ0Pl+GJIS73ybcJTIRaZTXkFRdhzOQkbY+Ox/2oSCosfDw1sREDHUC8MCPdHj2a+cLQ3+EI0q6VXe/C6desiPDxca0/2DRs2GK04Y2AYEVm+m0lZ6PrVgQrn73jrSczdfgX7rj6Ci9QOa1/ugKb+5X+fS7bz86i2GLfiVIXb2zOpExp4855KMh9mE5HlqyybdMmS0tuwhmxKzVZi67kEbIiNR+zddNV0R3tbPNPMF/3DAxAVIodtLbmfSeeh4ciRI/nQKiIyico6CuUWFOH759tg5NLjOHk7DSOXnsC6VzsgqMxzIUq2Y4wuR0REVLsZo9td6W1YQzZ5ONljRIcgjOgQhNvJ2dgYG49NZ+JxJyUHG2LjsSE2Ht4uEjzb0h8DWgegqZ9rjR4f6DxQWr58uQnLIKLaTJeOQg72tlgyqh2e+/EYLj/IwAs/H8e6VyPhK/v3soeS7ZTujFTR9oiIiLQxRre70tuwtmwKkjvh7acbYmK3UJy+m45NsfH461wCkjLzseRwHJYcjkNDH2cMCK+Dfq38a+T9v5bZgYGIapWSTkealO4oJHMQ45ex7RHk6Yj7abkY8fNxpJVq/Vqyndh76YgK0XwPpam6NRERUc2iazbpug1rzSaRSIQ29dzxaf/mOPFhN/w0si16tfCFva0Nrj3MwrztVxA1by+G/XgMv5+8ZxHfjBmLXl3vrBGvAycynZIbbjPyCqrc4lSf7kr3UnMwZFEMEjPy0DLQDb++GAFniZ1qO9M2X8B/Kuh6Z8puTUS6YjYRmY65sqmybZzS0vXOGrNJkVuAv88/wIbYeJyIS1VNl9jZoFtTHwwMD0B0Qy+Iba33exkOlIjIIJpapla1xak+3ZWuP8zEkMUxSM8pQFSIJ5aObgeJna1qOynZShQVCygqFlRdjqqjWxORLphNRKZh7myqbBvZ+QWQOdhDWVSM7PzCauskaGr303Kw+UwCNpy+j5uPslXTPZzs0TfMD/3DA9Aq0M3q7mfiQImI9KatZWp1tjg9cy8dz/90DNnKIjzTzBffDg+HnRV/ckW1B7OJyPgsJZtqM0EQcCE+Axti7+OvswlIzvr38vj6cif0Dw9A/1YBqOvpaMYqdceBEhHpzRgtU43lyI1kjFl2EsqiYgxpUwfzB4dZ3SdWVPswm4iMz5KyiYDComIcupGMTbHx2HExEXkF/3b9a1vPHQNaB6B3Cz+4WfDgtfY9OYqIqswYLVONJSpEjm+GhWP8r//gj3/uw81RjA97NeFgiYiolrGkbCLAztYGXRp5o0sjb2TlF2L7hURsio3HkZvJOHUnDafupGHGn5fQpbEXBoQHoEtjb9Ul9JaCAyUi0psxWqYa0zPNfTFvUBjeW3cOPx2Kg5ujPV7vElKtNRARkXlZWjbRv5wldhjcpg4Gt6mDREUe/jwbjw2n43ElMRM7Lj7EjosP4Sq1Q+8wfwxsHYA2dd1hYwEPtTXrxfzTp0+HSCRS+/H19VXNFwQB06dPh7+/PxwcHNC5c2dcvHjRjBUTEWB4y1RFjhI3k7IQezcNNx9lQZGj1LicIYa0DcRHvZsAAD7fcRWrjt2pdB1T1kNERNXLErNJX5ZUi6n4yqR4OboBtk+Mxt9vPYlXouvDx1WCjLxCrDlxF0MWxSD68334cudV3HyUZdZazXqP0vTp07Fu3Trs3r1bNc3W1hZeXl4AgHnz5uGzzz7D8uXL0bBhQ8yaNQsHDx7E1atX4eLiotM+eB04kWno2zLVFJ2INPlix1V8u+8GRCLg6+fC8WxL/wrrr456iDRhNhGZhqVmky4sqZbqVlQs4NitFGyMjcff5x8gW1mkmteyjgz9wwPQt6U/5M6Saq3L7AOlTZs24cyZM+XmCYIAf39/TJw4EZMnTwYA5Ofnw8fHB/PmzcMrr7yi0z4YRkSmo2vL1OrsRCQIAj7efAGrjt2FnY0IS0a1RedG3marh0gTZhOR6VhiNlXGkmoxt1xlEXZdfoiNp+/j4PVkFBU/HqrY2ojQqaEX+ocHoHtTH0jFpr+fyez3KF2/fh3+/v6QSCSIiIjA7NmzUb9+fcTFxSExMRHdu3dXLSuRSNCpUyccPXq0woFSfn4+8vPzVX/PyMgw+TEQ1VYyR92e/ZCcpdR48geAg9eTkZylNFoAiEQizHy2OTJyC/Hn2QS8uuofrBoXgbZBHmaphwhgNhFVJ0vMJmuqxdwc7G3xbEt/PNvSH8lZ+fjrbAI2xsbj3H0F9l5Jwt4rSXCW2GHdax3Q2Ne0HzSZ9R6liIgI/PLLL9ixYwd++uknJCYmIjIyEikpKUhMTAQA+Pj4qK3j4+OjmqfJnDlzIJPJVD+BgYEmPQYiqlx1dyKysRHhy6Et0bmRF/IKijFm+UlcSvj3jSk7I1F1YzYRWR5LygJLqsWSyJ0lGBMVjD8ndMTuSZ0woUsIAtwcIBXbIMTL9K3ezTpQ6tmzJwYNGoQWLVqgW7du2Lp1KwBgxYoVqmXKtvgVBEFr298pU6ZAoVCofu7du2ea4olIZ+boRCS2tcEPz7dB23ruyMwrxMilJ3A7Odts9VDtxmwisjyWlAWWVIulCvF2xrs9GuHQ+12wcXxUtTxg3qIeYe/k5IQWLVrg+vXrqu53Zb89SkpKKvctU2kSiQSurq5qP0RkXoZ2IqoqB3tb/Dy6HZr4uSI5Kx8v/HwciYo8s9VDtRezicjyWFIWWFItls7GRoRAD8fq2Ve17EVH+fn5uHz5Mvz8/BAcHAxfX1/s2rVLNV+pVOLAgQOIjIw0Y5VEpC+Zoz3mDgorFwIlnYhMed21zEGMX8a2R5CnI+6n5WLEz8dRLMBs9RARkWUwZzZZci30L7N2vXv33XfRt29f1K1bF0lJSZg1axYOHDiA8+fPo169epg3bx7mzJmDZcuWITQ0FLNnz8b+/fvZHpzISunaicgU7qXmYMiiGCRm5KFloBt+fTECRUXFZquHajdmE5HlMGc2WXItZOaud/fv38ewYcOQnJwMLy8vPPHEEzh27Bjq1asHAHj//feRm5uL8ePHIy0tDREREdi5c6fOgyQisiy6diIyhUAPR6wc1x5DFsfg7L10vLLyFJaObocG3qa/GZSIiCyXObOpLEuqhcz8jVJ14Kd2RFTamXvpeP6nY8hWFuGZZr74dnh4tdwQSlQas4mIyPLx3QER1SqtAt3w08i2sLe1wfaLiZiy4Txq+OdFREREZAAOlIio1okMkWPh8HDYiIA//rmP2dsuc7BEREREajhQIiKLoMhR4mZSFmLvpuHmoywocpQm3V+PZr6YNygMAPDToTh8v/+mSfdHRETWp7qziSyLWZs5EBEBQEJ6LiavP4dD15NV06JD5Zg7KAz+bg4m2++QtoFQ5BZg1tbL+HzHVcgcxHjhiXom2x8REVkPc2UTWQ5+o0REZqXIUZYLIgA4eD0ZH6w/Z/JP7158sj4mdAkBAHy8+QL+PJtg0v0REZHlM3c2kWXgQImIzCo5S1kuiEocvJ6M5CzTh9E73RvihSfqQhCASWvPYP/VJJPvk4iILJclZBOZHwdKRGRWGXkFWudnVjLfGEQiEWY+2xzPtvRHYbGAV1f9g1O3U02+XyIiskyWkE1kfhwoEZFZuUrFWue7VDLfWGxsRPhyaEt0buSFvIJijFl+EpcSMqpl30REZFksJZvIvDhQIiKzkjvbIzpUrnFedKgccufqe0K52NYGPzzfBm3ruSMzrxAjl57A7eTsats/ERFZBkvKJjIfDpSIyKxkjvaYOyisXCBFh8oxb1AYZI7VG0YO9rb4eXQ7NPFzRXJWPl74+TgSFXnVWgMREZmXpWUTmYdIqOFPWczIyIBMJoNCoYCrq6u5yyGiCihylEjOUiIzrwAuUjHkzvZmDaJHmfkYsugobqfkINTbGb+/0gHuTgxGMg5mE5F1sLRsourFgRIRUQXupeZg8KKjeJiRj1aBbvj1xQg4Sfj4Oao6ZhMRkeXjpXdERBUI9HDEqnERcHMU48y9dLyy8h/kFxaZuywiIiKqBhwoERFpEerjguVj2sPJ3haHbyTjrTVnUFhUbO6yiIiIyMQ4UCIiqkSrQDf8NLIt7G1tsP1iIqZuvIAaftUyERFRrceBEhGRDiJD5Fg4PBw2ImDtqXuY8/cV1WBJkaPEzaQsxN5Nw81HWVDk8IntRERkXsymquNdyUREOurRzBfzBoXhvXXn8OPBW3BzFKN/qwBMXn8Oh64nq5aLDpVj7qAw+Ls5mLFaIiKqrRLSc5lNRsCud0REelpy6BZmbb0MAAjxdsKNpPIPpY0OlWPhsHC2kSWNmE1EZCqKHCUmrIlVGySVYDbph5feERHp6cUn6+ONp0IAQOMgCQAOXk9GchYvcyAiouqVnKXUOEgCmE364kCJiMgAk55uiGea+2pdJjOvoJqqISIieiyjkuxhNumOAyUiIgOIRCK883RDrcu4SMXVVA0REdFjrpVkD7NJdxwoEREZyNtFgo4hnhrnRYfKIXfmNeBE/2vvvqOjqvP/j78mbVJIQgmkQOgRIiWUAAv4BV2aygoe+YEKKtWKa2JBVNgjB4EQFFYRy4HF0JSyqyDoSnFXiHxRiEgwYkwCBowghkBIMaTf3x8e5msmCS2ROzN5Ps6Zc8i9d25eH1l97Xvmzh0A11dQIy8NigiqcR/ddHUYlADgGgX6emnR/4vSwA5Vh6U+bZoofkx3PiwLALjuAn29tHBM92rD0qCIILrpKnF7cACog7DGPnpzQi8dzylSzMZDOn62SCfzLshisZgdDQDQQIU19tHr9/ZUTmGpCorL5O/tqaBGXgxJV4nbgwNAPTlTUKKxb+/T8bNFimjRSJse7q8mfpQSqqObAMDxcekdANST5v5WrZ3aTyEB3srILtSkVUn6taTc7FgAAOAaMCgBQD0Kb+qrtVP7qrGvpw5nnddDa79SSXmF2bEAAMBVYlACgHoWEeyvVZP7ys/LXf979Kxi1iervKLS7FgAAOAqMCgBcDh5RaU6ll2oQz/m6tiZQuUVOd+3iPcIb6zlD0TLy91N24+c1gubU+TiHwkFAJflCr2Eq8dd7wA4lFPnL2jm+9/o84wc27ZBEUFaOKa7whr7mJjs6g3sGKSl9/bUY+8e1KavflKgj6deuD2SO+IBgBNxpV7C1eEdJQAOI6+otFoZSVJiRo6ee/8bp3wF79auIVo4prskacXnmXpz9zGTEwEArpQr9hKuHIMSAIeRU1harYwuSszIUU6hcxbSuOhwzR4ZKUl6eUea3t1/wuREAIAr4aq9hCvDoATAYeQXl11yf8Fl9juyaf/TXo/f0lGSNHvLt9p2+JTJiQAAl+PKvYTLY1AC4DACvD0vud//Mvsd3dPDb9B9f2otw5Ce3Jis3WnZZkcCAFyCq/cSLo1BCYDDCGrkpUERQTXuGxQRpKBGXtc5Uf2yWCyaO6qrRkWFqbzS0CPrDuqr4+fMjgUAqIWr9xIujUEJgMMI9PXSwjHdq5XSoIggxY/prkBf5y8kNzeLFo+L0s2dmqu4rFKTVyXpu1P5ZscCANSgIfQSamcxXPyLPfLz8xUYGKi8vDwFBASYHQfAFcgrKlVOYakKisvk7+2poEZeLldGF0ordP/K/frqRK6CGln1r0f6q22Qn9mxcJ3QTYBzaQi9hOoYlADAJHkXynTP8i+V+nO+WjXx0b8eGaCQQG+zY+E6oJsAwPFx6R0AmCTQx1NrpvRV22a++in3gu5fuV+5v3KrWQAAHAGDEgCYqLm/VWun9lNwgFUZ2YWatCpJv5aUmx0LAIAGj0EJAEwW3tRX66b2U2NfTx3OOq+H1n6lkvIKs2MBANCgMSgBgAOICPbXqsl95eflrv89elYx65NVXlFpdiwAABosBiUAcBA9whtr+QPR8nJ30/Yjp/XC5hS5+P12AABwWAxKAOBABnYM0tJ7e8rNIm366ict+HcqwxIAACZgUAIAB3Nr1xAtHNNdkrTi80y9teeYyYkAAGh4GJQAwAGNiw7X7JGRkqRF29P07v4TJicCAKBhYVACAAc17X/a6/FbOkqSZm/5VtsOnzI5EQAADYeH2QEAwCx5RaXKKSxVfnGZAnw8FeTnpUBfL7NjVfH08Bt0/kKp1n35o57alCx/bw/d3KmF2bEAAH8AZ+ilhoRBCUCDdOr8Bc18/xt9npFj2zYoIkgLx3RXWGMfE5NVZbFYNHdUV+VdKNe2w6f0yLqDendaP/Vu09TsaACAeuQsvdSQcOkdgAYnr6i0WhlJUmJGjp57/xvlFZWalKxmbm4WLR4bpZs7NVdxWaUmJyQp9ed8s2MBAOqJs/VSQ8GgBKDBySksrVZGFyVm5Cin0PEKycvDTW9N6K3oNk2UX1yu+1ce0Imzv5odCwBQD5yxlxoCBiUADU5+cdkl9xdcZr9ZfLzctXJSH0WGBiinsET3rdyvX/KLzY4FAKgjZ+0lV8egBKDBCfD2vOR+/8vsN1Ogj6fWTOmrts18lXXugu5fuV/nuSQDAJyaM/eSK3OYQSkuLk4Wi0WxsbG2bYZhaM6cOQoLC5OPj49uvvlmHTlyxLyQAFxCUCMvDYoIqnHfoIggBTVy7DsMNfe3au3UfgoOsCr9l0JNSkjSryXlZscCAFwjZ+8lV+UQg1JSUpKWL1+u7t27V9m+aNEiLVmyRMuWLVNSUpJCQkI0bNgwFRQUmJQUgCsI9PXSwjHdq5XSoIggxY/p7hS3Yg1v6qu1U/upsa+nkrPO6+G1B1VSXmF2LADANXCFXnJFFsMwDDMDFBYWqlevXnrzzTc1b9489ejRQ6+++qoMw1BYWJhiY2M1c+ZMSVJJSYmCg4MVHx+vhx9++IrOn5+fr8DAQOXl5SkgIOCPXAoAJ3Px+yoKisvk7+2poEbO930VyVnnNX7FlyoqrdBtXUO0bHwvubtZzI6Fy6CbANTEFXrJlZj+jtL06dM1cuRIDR06tMr2zMxMnT59WsOHD7dts1qtGjx4sPbt21fr+UpKSpSfn1/lAQA1CfT1UocWjdSjdRN1aNHIKcuoR3hjrXggWl7ubvrk29N64YMUmfz6F2pANwG4Eq7QS67E1EFpw4YN+vrrrxUXF1dt3+nTpyVJwcHBVbYHBwfb9tUkLi5OgYGBtkd4eHj9hgYABzOwY5CW3ttDbhZp41dZivvke4YlB0M3AYDzMW1QysrKUkxMjNatWydvb+9aj7NYql5CYhhGtW2/9/zzzysvL8/2yMrKqrfMAOCobu0aqoV3/fY5z+WJP+itPcdMToTfo5sAwPl4mPWLDx48qOzsbPXu3du2raKiQomJiVq2bJnS0tIk/fbOUmhoqO2Y7Ozsau8y/Z7VapXVav3jggOAgxrXJ1x5F8o0/9+pWrQ9TYE+nprQr43ZsSC6CQCckWnvKA0ZMkQpKSlKTk62PaKjozVhwgQlJyerffv2CgkJ0a5du2zPKS0t1Z49ezRgwACzYgOAQ3twUHtNv6WDJGn2lm+17fApkxMBAOCcTHtHyd/fX127dq2yzc/PT82aNbNtj42N1YIFCxQREaGIiAgtWLBAvr6+Gj9+vBmRAcApPDO8k84Xlend/T/qqU3J8vf20M2dWpgdCwAAp2LaoHQlnn32WV24cEGPPfaYcnNz1a9fP+3cuVP+/v5mRwMAh2WxWDR3dFflF5dr2+FTemTdQa2b2k/RbZuaHQ0AAKdh+vco/dH4rgoADVVpeaUeWvuVdqedUYC3hzY+3F+Rofx30BHQTQDg+Ez/HiUAwB/Dy8NNb03oreg2TZRfXK77Vx7Q8ZxfzY4FAIBTYFACABfm4+WulZP6KDI0QDmFJbpv5X6dzis2OxYAAA6PQQkAXFygj6fWTOmrts189VPuBT3wzn6dLyo1OxYAAA6NQQkAGoDm/latndpPwQFWpf9SqEkJSfq1pNzsWAAAOCwGJQBoIMKb+mrd1H5q7Oup5KzzenjtQZWUV5gdCwAAh8SgBAANSESwv1ZN7itfL3ftPZqjmPXJKq+oNDsWAAAOh0EJABqYHuGNteKBaHm5u2n7kdOatflbufg3RQAAcNUYlACgARrYMUhL7+0hN4u08assxX3yPcMSAAC/w6AEAA3UrV1DtfCu7pKk5Yk/6K09x0xOBACA42BQAoAGbFyfcM26PVKStGh7mt7df8LkRAAAOAYGJQBo4B4c1F7Tb+kgSZq95VttO3zK5EQAAJiPQQkAoGeGd9KEfq1lGNJTm5K1Oy3b7EgAAJiKQQkAIIvFormju+ov3UNVVmHokXUHdfDEObNjAQBgGgYlAIAkyd3NoiXjemjwDc1VXFapyQlJSv053+xYAACYgkEJAGDj5eGmt+/rreg2TZRfXK77Vx7Q8ZxfzY4FAMB1x6AEAKjCx8tdKyf1UecQf+UUlujT1F/MjgQAwHXnYXYAAIDjCfTx1JqpffWf1Gzd27e12XEAALjueEcJAFCjFv7eDEkAgAaLQQkAAAAA7DAoAQAAAIAdBiUAAAAAsMOgBAAAAAB2GJQAAAAAwA6DEgAAAADYYVACAAAAADsMSgAAAABgh0EJAAAAAOwwKAEAAACAHQYlAAAAALDDoAQAAAAAdhiUAAAAAMAOgxIAAAAA2PEwO8AfzTAMSVJ+fr7JSQCgYfL395fFYjE7hkOhmwDAXFfSTS4/KBUUFEiSwsPDTU4CAA1TXl6eAgICzI7hUOgmADDXlXSTxbj4spaLqqys1KlTp675Fc38/HyFh4crKyvLZYreFdckuea6XHFNkmuuizXVjneUqqObqnPFNUmuuS7W5DxccV3Xs5tc/h0lNzc3tWrVqs7nCQgIcJn/gV3kimuSXHNdrrgmyTXXxZpwJeim2rnimiTXXBdrch6uuK7rsSZu5gAAAAAAdhiUAAAAAMAOg9JlWK1Wvfjii7JarWZHqTeuuCbJNdflimuSXHNdrAnXkyv+3bjimiTXXBdrch6uuK7ruSaXv5kDAAAAAFwt3lECAAAAADsMSgAAAABgh0EJAAAAAOwwKAEAAACAHQalKxAXFyeLxaLY2Fizo9TJnDlzZLFYqjxCQkLMjlVnJ0+e1H333admzZrJ19dXPXr00MGDB82OVSdt27at9ndlsVg0ffp0s6Nds/Lycs2ePVvt2rWTj4+P2rdvr7lz56qystLsaHVSUFCg2NhYtWnTRj4+PhowYICSkpLMjnVVEhMTdccddygsLEwWi0Vbtmypst8wDM2ZM0dhYWHy8fHRzTffrCNHjpgTFjZ0k2Ojm5wD3eS4HKGbGJQuIykpScuXL1f37t3NjlIvunTpop9//tn2SElJMTtSneTm5mrgwIHy9PTUJ598ou+++06LFy9W48aNzY5WJ0lJSVX+nnbt2iVJGjt2rMnJrl18fLzefvttLVu2TKmpqVq0aJFefvllvf7662ZHq5Np06Zp165dWrt2rVJSUjR8+HANHTpUJ0+eNDvaFfv1118VFRWlZcuW1bh/0aJFWrJkiZYtW6akpCSFhIRo2LBhKigouM5JcRHd5NjoJudBNzkuh+gmA7UqKCgwIiIijF27dhmDBw82YmJizI5UJy+++KIRFRVldox6NXPmTOOmm24yO8YfLiYmxujQoYNRWVlpdpRrNnLkSGPKlClVtt11113GfffdZ1KiuisqKjLc3d2Njz76qMr2qKgoY9asWSalqhtJxubNm20/V1ZWGiEhIcbChQtt24qLi43AwEDj7bffNiEh6CbHRzc5D7rJOZjVTbyjdAnTp0/XyJEjNXToULOj1JuMjAyFhYWpXbt2uueee/TDDz+YHalOtm7dqujoaI0dO1YtWrRQz549tWLFCrNj1avS0lKtW7dOU6ZMkcViMTvONbvpppv0n//8R+np6ZKkw4cPa+/evbr99ttNTnbtysvLVVFRIW9v7yrbfXx8tHfvXpNS1a/MzEydPn1aw4cPt22zWq0aPHiw9u3bZ2Kyhotucnx0k/Ogm5zT9eomj3o7k4vZsGGDvv76a6e7nvNS+vXrpzVr1uiGG27QL7/8onnz5mnAgAE6cuSImjVrZna8a/LDDz/orbfe0lNPPaUXXnhBBw4c0BNPPCGr1aoHHnjA7Hj1YsuWLTp//rwmTZpkdpQ6mTlzpvLy8tS5c2e5u7uroqJC8+fP17333mt2tGvm7++v/v3766WXXlJkZKSCg4O1fv167d+/XxEREWbHqxenT5+WJAUHB1fZHhwcrBMnTpgRqUGjm5wD3eQ86CbndL26iUGpBllZWYqJidHOnTurTePO7LbbbrP9uVu3burfv786dOig1atX66mnnjIx2bWrrKxUdHS0FixYIEnq2bOnjhw5orfeestlymjlypW67bbbFBYWZnaUOtm4caPWrVun9957T126dFFycrJiY2MVFhamiRMnmh3vmq1du1ZTpkxRy5Yt5e7url69emn8+PH6+uuvzY5Wr+xfMTYMw6lfRXZGdJPzoJucB93k3P7obuLSuxocPHhQ2dnZ6t27tzw8POTh4aE9e/Zo6dKl8vDwUEVFhdkR64Wfn5+6deumjIwMs6Ncs9DQUN14441VtkVGRurHH380KVH9OnHihD799FNNmzbN7Ch1NmPGDD333HO655571K1bN91///168sknFRcXZ3a0OunQoYP27NmjwsJCZWVl6cCBAyorK1O7du3MjlYvLt597OKrdxdlZ2dXeyUPfyy6yXnQTc6DbnJO16ubGJRqMGTIEKWkpCg5Odn2iI6O1oQJE5ScnCx3d3ezI9aLkpISpaamKjQ01Owo12zgwIFKS0ursi09PV1t2rQxKVH9SkhIUIsWLTRy5Eizo9RZUVGR3Nyq/ifH3d3d6W/BepGfn59CQ0OVm5urHTt2aPTo0WZHqhft2rVTSEiI7e5W0m+fTdizZ48GDBhgYrKGh25yHnST86CbnNP16iYuvauBv7+/unbtWmWbn5+fmjVrVm27M3nmmWd0xx13qHXr1srOzta8efOUn5/v1G8tP/nkkxowYIAWLFigcePG6cCBA1q+fLmWL19udrQ6q6ysVEJCgiZOnCgPD+f/V/WOO+7Q/Pnz1bp1a3Xp0kWHDh3SkiVLNGXKFLOj1cmOHTtkGIY6deqko0ePasaMGerUqZMmT55sdrQrVlhYqKNHj9p+zszMVHJyspo2barWrVsrNjZWCxYsUEREhCIiIrRgwQL5+vpq/PjxJqZueOgm50E3OQ+6yXE5RDfV2/3zXJwr3IL17rvvNkJDQw1PT08jLCzMuOuuu4wjR46YHavOtm3bZnTt2tWwWq1G586djeXLl5sdqV7s2LHDkGSkpaWZHaVe5OfnGzExMUbr1q0Nb29vo3379sasWbOMkpISs6PVycaNG4327dsbXl5eRkhIiDF9+nTj/PnzZse6Kp999pkhqdpj4sSJhmH8dhvWF1980QgJCTGsVqsxaNAgIyUlxdzQMAyDbnJkdJNzoJsclyN0k8UwDKP+xi4AAAAAcH58RgkAAAAA7DAoAQAAAIAdBiUAAAAAsMOgBAAAAAB2GJQAAAAAwA6DEgAAAADYYVACAAAAADsMSgAAAABgh0EJqAOLxaItW7aYHUOSlJaWppCQEBUUFEiSVq1apcaNG5sbqhbLli3TqFGjzI4BAC6Jbro2dBPsMSjBqU2aNEkWi0WPPPJItX2PPfaYLBaLJk2adP2DmWDWrFmaPn26/P39zY5yWQ8++KCSkpK0d+9es6MAQL2jm/4P3QRnxqAEpxceHq4NGzbowoULtm3FxcVav369WrdubWKy6+enn37S1q1bNXnyZLOjqKKiQpWVlZc8xmq1avz48Xr99devUyoAuL7oJroJzo9BCU6vV69eat26tT744APbtg8++EDh4eHq2bOnbduaNWvUrFkzlZSUVHn+mDFj9MADD9R47tLSUj3++OMKDQ2Vt7e32rZtq7i4uFqznDx5UnfffbeaNGmiZs2aafTo0Tp+/HiVYxISEhQZGSlvb2917txZb775pm3f8ePHZbFYtGHDBg0YMEDe3t7q0qWLdu/efcl/Bps2bVJUVJRatWpVbd+OHTsUGRmpRo0a6dZbb9XPP/9s21dZWam5c+eqVatWslqt6tGjh7Zv327bv3v3blksFp0/f962LTk5WRaLxbaui5dRfPTRR7rxxhtltVp14sQJ7d69W3379pWfn58aN26sgQMH6sSJE7bzjBo1Slu2bKnyfyIAwFXQTXQTnB+DElzC5MmTlZCQYPv5nXfe0ZQpU6ocM3bsWFVUVGjr1q22bTk5Ofroo49qfbVr6dKl2rp1qzZt2qS0tDStW7dObdu2rfHYoqIi3XLLLWrUqJESExO1d+9eWwGUlpZKklasWKFZs2Zp/vz5Sk1N1YIFC/S3v/1Nq1evrnKuGTNm6Omnn9ahQ4c0YMAAjRo1SmfPnq11/YmJiYqOjq4x0yuvvKK1a9cqMTFRP/74o5555hnb/tdee02LFy/WK6+8om+++UYjRozQqFGjlJGRUevvqm3tcXFx+sc//qEjR46oadOmuvPOOzV48GB98803+uKLL/TQQw/JYrHYnhMdHa2ysjIdOHDgqn4XADgLuolugpMzACc2ceJEY/To0caZM2cMq9VqZGZmGsePHze8vb2NM2fOGKNHjzYmTpxoO/7RRx81brvtNtvPr776qtG+fXujsrKyxvP/9a9/Nf785z/Xul+SsXnzZsMwDGPlypVGp06dqhxbUlJi+Pj4GDt27DAMwzDCw8ON9957r8o5XnrpJaN///6GYRhGZmamIclYuHChbX9ZWZnRqlUrIz4+vtZ/DlFRUcbcuXOrbEtISDAkGUePHrVte+ONN4zg4GDbz2FhYcb8+fOrPK9Pnz7GY489ZhiGYXz22WeGJCM3N9e2/9ChQ4YkIzMzs8rvSU5Oth1z9uxZQ5Kxe/fuWjMbhmE0adLEWLVq1SWPAQBnQzf9hm6Cs/MwYzgD6ltQUJBGjhyp1atXyzAMjRw5UkFBQdWOe/DBB9WnTx+dPHlSLVu2VEJCgu1DtzWZNGmShg0bpk6dOunWW2/VX/7yFw0fPrzGYw8ePKijR49W+8BqcXGxjh07pjNnzigrK0tTp07Vgw8+aNtfXl6uwMDAKs/p37+/7c8eHh6Kjo5Wampqreu/cOGCvL29q2339fVVhw4dbD+HhoYqOztbkpSfn69Tp05p4MCBVZ4zcOBAHT58uNbfVRMvLy91797d9nPTpk01adIkjRgxQsOGDdPQoUM1btw4hYaGVnmej4+PioqKrup3AYCzoJvoJjg3BiW4jClTpujxxx+XJL3xxhs1HtOzZ09FRUVpzZo1GjFihFJSUrRt27Zaz9mrVy9lZmbqk08+0aeffqpx48Zp6NCh+te//lXt2MrKSvXu3VvvvvtutX3NmzdXcXGxpN8ucejXr1+V/e7u7pddX22FKf1Wxrm5udW2e3p6VjuHYRiXPK9hGLZtbm5utm0XlZWVVfs9Pj4+1c6TkJCgJ554Qtu3b9fGjRs1e/Zs7dq1S3/6059sx5w7d07NmzevdV0A4OzoJroJzovPKMFlXLzeurS0VCNGjKj1uGnTpikhIUHvvPOOhg4dqvDw8EueNyAgQHfffbdWrFihjRs36v3339e5c+eqHderVy9lZGSoRYsW6tixY5VHYGCggoOD1bJlS/3www/V9rdr167Kub788kvbn8vLy3Xw4EF17ty51ow9e/bUd999d8l11LSusLCwardB3bdvnyIjIyXJVhS//5BtcnLyFf+Onj176vnnn9e+ffvUtWtXvffee7Z9x44dU3FxcZUPNQOAq6Gb6CY4LwYluAx3d3elpqYqNTX1kq+CTZgwQSdPntSKFSuqfajW3t///ndt2LBB33//vdLT0/XPf/5TISEhNX5Z3oQJExQUFKTRo0fr888/V2Zmpvbs2aOYmBj99NNPkqQ5c+YoLi5Or732mtLT05WSkqKEhAQtWbKkyrneeOMNbd68Wd9//72mT5+u3NzcS2YdMWKEvvjiC1VUVFxyPfZmzJih+Ph4bdy4UWlpaXruueeUnJysmJgYSVLHjh0VHh6uOXPmKD09XR9//LEWL1582fNmZmbq+eef1xdffKETJ05o586dSk9Pt5WcJH3++edq3759lcsvAMDV0E10E5wXl97BpQQEBFzRMWPGjNHHH3+sO++885LHNmrUSPHx8crIyJC7u7v69Omjf//737a3/X/P19dXiYmJmjlzpu666y4VFBSoZcuWGjJkiC3XtGnT5Ovrq5dfflnPPvus/Pz81K1bN8XGxlY518KFCxUfH69Dhw6pQ4cO+vDDD2u8rv2i22+/XZ6envr0008v+YqlvSeeeEL5+fl6+umnlZ2drRtvvFFbt25VRESEpN8uj1i/fr0effRRRUVFqU+fPpo3b57Gjh17yfP6+vrq+++/1+rVq3X27FmFhobq8ccf18MPP2w7Zv369VWuhwcAV0U30U1wThbD/qJQoAEYNmyYIiMjtXTpUrOjVHH8+HG1a9dOhw4dUo8ePa7quW+++aY+/PBD7dix448JV4++/fZbDRkyROnp6dU+LAwADRXdZC66CfZ4RwkNyrlz57Rz507997//1bJly8yOU68eeugh5ebmqqCgoNrdjRzNqVOntGbNGooIAEQ3OQq6CfYYlNCg9OrVS7m5uYqPj1enTp3MjlOvPDw8NGvWLLNjXJHabmMLAA0R3eQY6CbY49I7AAAAALDDXe8AAAAAwA6DEgAAAADYYVACAAAAADsMSgAAAABgh0EJAAAAAOwwKAEAAACAHQYlAAAAALDDoAQAAAAAdv4/VAYhuIyZRKsAAAAASUVORK5CYII=", - "application/papermill.record/text/plain": "<Figure size 1000x500 with 2 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sleep_regressions_1-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np \n", "import matplotlib.pyplot as plt\n", @@ -396,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "ffd9dca1", "metadata": { "editable": true, @@ -407,21 +298,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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{\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.96</td>\n", - " <td>3.02</td>\n", - " <td>41.76</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>119.97</td>\n", - " <td>131.94</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.94</td>\n", - " <td>0.43</td>\n", - " <td>-20.85</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-9.79</td>\n", - " <td>-8.09</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 125.96 3.02 41.76 0.0 0.82 0.81 119.97 131.94\n", - "1 dan_sleep -8.94 0.43 -20.85 0.0 0.82 0.81 -9.79 -8.09" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Display results, rounded to two decimal places.\n", "mod1.round(2)" @@ -681,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "f27c3f32", "metadata": { "editable": true, @@ -690,79 +495,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>12.78</td>\n", - " <td>0.28</td>\n", - " <td>45.27</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>12.22</td>\n", - " <td>13.34</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_grump</td>\n", - " <td>-0.09</td>\n", - " <td>0.00</td>\n", - " <td>-20.85</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-0.10</td>\n", - " <td>-0.08</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 12.78 0.28 45.27 0.0 0.82 0.81 12.22 13.34\n", - "1 dan_grump -0.09 0.00 -20.85 0.0 0.82 0.81 -0.10 -0.08" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "modx = pg.linear_regression(df['dan_grump'], df['dan_sleep'])\n", "modx.round(2)" @@ -862,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "f9c9eddf", "metadata": { "editable": true, @@ -895,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "49eff9c6", "metadata": { "editable": true, @@ -929,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "b3dddf41", "metadata": { "editable": true, @@ -963,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "3acaa6b8", "metadata": { "editable": true, @@ -972,92 +705,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.97</td>\n", - " <td>3.04</td>\n", - " <td>41.42</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>119.93</td>\n", - " <td>132.00</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.95</td>\n", - " <td>0.55</td>\n", - " <td>-16.17</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-10.05</td>\n", - " <td>-7.85</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>baby_sleep</td>\n", - " <td>0.01</td>\n", - " <td>0.27</td>\n", - " <td>0.04</td>\n", - " <td>0.97</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-0.53</td>\n", - " <td>0.55</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 125.97 3.04 41.42 0.00 0.82 0.81 119.93 132.00\n", - "1 dan_sleep -8.95 0.55 -16.17 0.00 0.82 0.81 -10.05 -7.85\n", - "2 baby_sleep 0.01 0.27 0.04 0.97 0.82 0.81 -0.53 0.55" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mod2.round(2)" ] @@ -1079,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "4757a6d2", "metadata": { "editable": true, @@ -1090,21 +738,7 @@ 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", - "application/papermill.record/text/plain": "<Figure size 2500x1000 with 1 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "sleep_regressions_3d-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", @@ -1270,7 +904,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "4d5fe885", "metadata": { "editable": true, @@ -1302,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "3c11290e", "metadata": { "editable": true, @@ -1311,79 +945,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.96</td>\n", - " <td>3.02</td>\n", - " <td>41.76</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>119.97</td>\n", - " <td>131.94</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.94</td>\n", - " <td>0.43</td>\n", - " <td>-20.85</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-9.79</td>\n", - " <td>-8.09</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 125.96 3.02 41.76 0.0 0.82 0.81 119.97 131.94\n", - "1 dan_sleep -8.94 0.43 -20.85 0.0 0.82 0.81 -9.79 -8.09" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mod1 = pg.linear_regression(X, Y)\n", "mod1.round(2)" @@ -1406,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "id": "6f66d0de", "metadata": { "editable": true, @@ -1438,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "id": "bcce8ffe", "metadata": { "editable": true, @@ -1447,18 +1009,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "1838.7224883200004" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "SS_resid = sum( (Y - Y_pred)**2 )\n", "SS_resid" @@ -1481,7 +1032,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "738f942e", "metadata": { "editable": true, @@ -1490,18 +1041,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "9998.590000000002" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "SS_tot = sum( (Y - np.mean(Y))**2 )\n", @@ -1547,7 +1087,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "id": "a0ddbf1a", "metadata": { "editable": true, @@ -1556,18 +1096,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.816101821524835" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "R2 = 1- (SS_resid / SS_tot)\n", "R2" @@ -1611,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "ce50df54", "metadata": { "editable": true, @@ -1620,18 +1149,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8161027191478786" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "r = X.corr(Y) # calculate the correlation\n", "r**2 # print the squared correlation" @@ -1836,7 +1354,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "eb455782", "metadata": { "editable": true, @@ -1845,15 +1363,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "F= 215.2382865368443 p= 2.1457300163209325e-36\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "from scipy import stats as st\n", @@ -1915,7 +1425,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "id": "387b7ea4", "metadata": { "editable": true, @@ -1959,7 +1469,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "id": "50ffea08", "metadata": { "editable": true, @@ -1968,18 +1478,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "(215.2382865368443, 2.1457300163209325e-36)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "predictors = df[['dan_sleep', 'baby_sleep']]\n", "outcome = df['dan_grump']\n", @@ -2006,7 +1505,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "id": "5fc70431", "metadata": { "editable": true, @@ -2015,92 +1514,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.97</td>\n", - " <td>3.04</td>\n", - " <td>41.42</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>119.93</td>\n", - " <td>132.00</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.95</td>\n", - " <td>0.55</td>\n", - " <td>-16.17</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-10.05</td>\n", - " <td>-7.85</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>baby_sleep</td>\n", - " <td>0.01</td>\n", - " <td>0.27</td>\n", - " <td>0.04</td>\n", - " <td>0.97</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-0.53</td>\n", - " <td>0.55</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 125.97 3.04 41.42 0.00 0.82 0.81 119.93 132.00\n", - "1 dan_sleep -8.95 0.55 -16.17 0.00 0.82 0.81 -10.05 -7.85\n", - "2 baby_sleep 0.01 0.27 0.04 0.97 0.82 0.81 -0.53 0.55" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "predictors = df[['dan_sleep', 'baby_sleep']]\n", "outcome = df['dan_grump']\n", @@ -2187,7 +1601,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "id": "782d33f1", "metadata": { "editable": true, @@ -2196,92 +1610,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.97</td>\n", - " <td>3.04</td>\n", - " <td>41.42</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>119.93</td>\n", - " <td>132.00</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.95</td>\n", - " <td>0.55</td>\n", - " <td>-16.17</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-10.05</td>\n", - " <td>-7.85</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>baby_sleep</td>\n", - " <td>0.01</td>\n", - " <td>0.27</td>\n", - " <td>0.04</td>\n", - " <td>0.97</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-0.53</td>\n", - " <td>0.55</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 125.97 3.04 41.42 0.00 0.82 0.81 119.93 132.00\n", - "1 dan_sleep -8.95 0.55 -16.17 0.00 0.82 0.81 -10.05 -7.85\n", - "2 baby_sleep 0.01 0.27 0.04 0.97 0.82 0.81 -0.53 0.55" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mod2.round(2)" ] @@ -2320,7 +1649,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "id": "1e7e0a09", "metadata": { "editable": true, @@ -2329,15 +1658,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "F: 215.24 p: 2.1457300163209325e-36\n" - ] - } - ], + "outputs": [], "source": [ "f = regression_f(predictors, outcome)\n", "print(\"F:\", f[0].round(2), \"p:\", f[1])" @@ -2381,7 +1702,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "dbac25b8", "metadata": { "editable": true, @@ -2390,79 +1711,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.96</td>\n", - " <td>3.02</td>\n", - " <td>41.76</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>119.97</td>\n", - " <td>131.94</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.94</td>\n", - " <td>0.43</td>\n", - " <td>-20.85</td>\n", - " <td>0.0</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-9.79</td>\n", - " <td>-8.09</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 125.96 3.02 41.76 0.0 0.82 0.81 119.97 131.94\n", - "1 dan_sleep -8.94 0.43 -20.85 0.0 0.82 0.81 -9.79 -8.09" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "X = df['dan_sleep'] # the predictor\n", "Y = df['dan_grump'] # the outcome\n", @@ -2487,7 +1736,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "77497fa4", "metadata": { "editable": true, @@ -2496,60 +1745,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>n</th>\n", - " <th>r</th>\n", - " <th>CI95%</th>\n", - " <th>p-val</th>\n", - " <th>BF10</th>\n", - " <th>power</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>pearson</th>\n", - " <td>100</td>\n", - " <td>-0.903384</td>\n", - " <td>[-0.93, -0.86]</td>\n", - " <td>8.176426e-38</td>\n", - " 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<td>0.0559</td>\n", - " <td>0.0388</td>\n", - " <td>0.9691</td>\n", - " <td>0.8161</td>\n", - " <td>0.8123</td>\n", - " <td>-0.1089</td>\n", - " <td>0.1132</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 \\\n", - "0 Intercept -0.0000 0.0435 -0.0000 1.0000 0.8161 0.8123 \n", - "1 dan_sleep_standard -0.9047 0.0559 -16.1715 0.0000 0.8161 0.8123 \n", - "2 baby_sleep_standard 0.0022 0.0559 0.0388 0.9691 0.8161 0.8123 \n", - "\n", - " CI[2.5%] CI[97.5%] \n", - "0 -0.0864 0.0864 \n", - "1 -1.0158 -0.7937 \n", - "2 -0.1089 0.1132 " - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from scipy import stats\n", "import pingouin as pg\n", @@ -3113,7 +2062,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "3742285c", "metadata": { "editable": true, @@ -3165,7 +2114,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "930915eb", "metadata": { "editable": true, @@ -3236,7 +2185,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "e1e357d9", "metadata": { "editable": true, @@ -3245,82 +2194,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>student_resid</th>\n", - " <th>unadj_p</th>\n", - " <th>bonf(p)</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>-0.494821</td>\n", - " <td>0.621857</td>\n", - " <td>1.0</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>1.105570</td>\n", - " <td>0.271676</td>\n", - " <td>1.0</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>0.461729</td>\n", - " <td>0.645321</td>\n", - " <td>1.0</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>-0.475346</td>\n", - " <td>0.635620</td>\n", - " <td>1.0</td>\n", - " </tr>\n", - " <tr>\n", - " <th>4</th>\n", - " <td>0.166721</td>\n", - " <td>0.867940</td>\n", - " <td>1.0</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " student_resid unadj_p bonf(p)\n", - "0 -0.494821 0.621857 1.0\n", - "1 1.105570 0.271676 1.0\n", - "2 0.461729 0.645321 1.0\n", - "3 -0.475346 0.635620 1.0\n", - "4 0.166721 0.867940 1.0" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "stud_res = est.outlier_test()\n", "stud_res.head()" @@ -3363,7 +2237,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "dcc141dc", "metadata": { "editable": true, @@ -3374,21 +2248,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "outlier-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -3489,7 +2349,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "ee9dcdf5", "metadata": { "editable": true, @@ -3500,21 +2360,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "<Figure size 1000x500 with 2 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "leverage-influence-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", @@ -3702,7 +2548,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "fbb75951", "metadata": { "editable": true, @@ -3713,21 +2559,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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Xg5uxrqY1riIiJcSOHTt44oknjC5DRFzEW6sP89PR8wT6evFh/2hCA2/cjOXt6U23mt1y37c3BVcRkRLijz/+oHz58kaXISIu4Ns9cczZeByAt3o3pk7FwjVj+Xr58lrr14qtLgVXEZESYteuXUaXICIu4NezFxkTY2vGeqZdTR5sZFwz1tUUXEVEREQEgOR0WzNWRraZNhHhjO5YtGYsq9VKRk4GAAHeAXbf8lXNWSIiIiKC2WJlxIJdnEpKp0pYANP7Fb0ZKyMngxZftKDFFy1yA6w9KbiKiIiICFPXHGbTkUQCfGzNWKUDfY0u6RoKriIiIiIl3Hd7zzJr/TEApvRuxJ23lTK4ovwpuIqIiIiUYIf/vMTomD0APN22Bt0aVzK4ooIpuIqIiIiUUCnp2Tw9L5Z0k5nWtcIZU8RmLEdTcBUREREpgcwWK88t3MXJ8+ncXsbWjOXt5dzR0LmrExEREZFi8e73v7H+cAL+Pp7M6d+UMkHO14x1Nd3HVURERKSEWbX/LB+sOwrAlF6NqF8p1C7n9fL04r6q9+W+b28KriIiIiIlyG/xl3h+ka0Za1Dr6nSPrGy3c/t5+fHO3e/Y7XxX01IBERERkRIiJSObwfN2kG4y07JmWcZ2qmt0SUWi4CoiIiJSAlgsVkYt3M3viWlULh3AB482cfpmrKu5VrUiIiIiclPe++EIPx46h5+3rRkrrBiasdKz02n4aUMaftqQ9Ox0u59fwVVERETEza058CfTfjgCwJu9GtKgsn2asRxNwVVERETEjR09979mrIGtqvFQ1O0GV3TzFFxFRERE3NTFzGyenreD1Kwc7qoRxvjOdxpd0i1RcBURERFxQxaLlecX7uZ4QhqVQv354NEm+LhYM9bVXLt6EREREcnXtB+PsPbXc/h6ezK7f1PCg/2MLumWKbiKiIiIuJnvD8bz3lpbM9YbDzWk0e2ljS3ITrRzloiIiIgbOZaQyvMLdwPwZMtq9GrquGYsL08v2lRuk/u+vRky47p161b69OlDkyZNaNWqFZMnTyYzMzPfsRs2bKBr165ERkbSqVMn1q1b5+BqRURERFzDpcxsnv4slktZOTSvFsaEBx3bjOXn5cfMDjOZ2WEmfl72X5rg8OCalJTE4MGD6devH7GxsSxZsoTt27fz4YcfXjP2xIkTjBgxgueee47Y2FhGjBjByJEjiY+Pd3TZIiIiIk7NYrHy/KI9HEtIo2Ipf2Y85vrNWFdz+GcTFhbGli1b6NmzJx4eHiQnJ5OVlUVYWNg1Y5csWUJ0dDQdOnTA29ubzp0706xZMxYuXOjoskVERESc2gfrjvL9wXh8vWzNWOVCXL8Z62qGrHENDg4GoF27dsTHxxMdHU3Pnj2vGXf06FFq166d57FatWpx6NAhh9QpIiIi4gp++DWed9f+BsCrPRoQWaW0IXWkZ6dz96K7AVj/8HoCfQLten5D54/XrFnDxo0b8fT05Nlnn73m+bS0NAICAvI85u/vT3q6/fe+FREREXFFxxNSGfnlbqxW6H9XVR5uVsXQejJyMsjIySiWcxsaXP39/alQoQKjR49m06ZNpKSk5Hk+ICDgmqatzMxMgoKCHFmmiIiIiFNKzcrh6Xk7uJSVQ3TVMkzsUs/okoqVw4Przp07eeCBBzCZTLmPmUwmfHx8rpldrV27NkeOHMnz2NGjR4mIiHBIrSIiIiLOymKx8o9Fuzl6LpUKpfyY+XgTfL3dqxnrag7/7OrUqUNmZiZvv/02JpOJM2fOMGXKFHr37o2vr2+esd26dWP79u2sWLGCnJwcVqxYwfbt2+nevbujyxYRERFxKrM2HGP1gXh8vDyY9XhTyof4G11SsXN4cA0KCmLu3LkcOXKEVq1a0b9/f1q2bMn48eMBiIqK4ptvvgGgZs2azJgxgzlz5tCsWTNmzpzJ9OnTqV69uqPLFhEREXEa6w6dY+qawwC80r0BTe4oY3BFjmHIXQVq1arFJ598ku9zu3btynPcpk0b2rRp44iyRERERJzeicQ0nv1yF1Yr9Gt+B/2a32F0SQ6jLV9FREREXERaVg5Pz4vlUmYOTe4ozb+6OVczlqeHJ9EVonPftzcFVxEREREXYLVaGR2zh9/iUykX4sesx5vi5+1ldFl5+Hv7858H/lNs53fv1jMRERERNzF7w3FW7PsTHy8PZj/ehAql3L8Z62oKriIiIiJObsNvCfx7tW3n0H91q0/TqmEGV2QMBVcRERERJ3byfBojvtiJ1QqPNKvCo07cjJWenU7bL9vS9su2pGfbf6dTrXEVERERcVLpphwGz9vBxcwcIquUZlL3+nh4eBhd1nVdyLpQbOfWjKuIiIiIE7I1Y+3l0J+XCA/2Y7YTNmM5moKriIiIiBP6cONxvtt7Fm9PD2Y93oSKoSWvGetqCq4iIiIiTmbTkQSmrLI1Y73ctR7NqpXMZqyrKbiKiIiIOJHTSemMWLALixUejr6dx++qanRJTkPBVURERMRJpJtyeOqzWJLTs2l8eyivdG/g9M1YjqTgKiJSAiQnJzNmzBhatGhBs2bNGDp0KOfOnTO6LBG5gtVqZezX+/5qxvJldv+m+Pu4VjOWp4cn9cvWp37Z+sWy5auCq4hICTBixAjS09P5/vvvWbduHV5eXkycONHoskTkCnM3/c43e+Lw9vRgxqNNuC00wOiSiszf258vu3zJl12+xN/b/s1kuo+riIib279/P3v27GHLli0EBwcDMHnyZBISEgyuTEQu++loIm+s/BWAiV3q0aJGWYMrck6acRURcXN79+6lVq1aLFq0iPvuu4/WrVszZcoUypUrZ3RpIoKtGWv4FzuxWKFXk9t54m9qxiqIgquIiJtLSUnh8OHDnDhxgiVLlrB06VLi4+N58cUXjS5NpMTLMJkZPG8HF9KzaXR7KK895NrNWBk5GXSM6UjHmI5k5GTY/fwKriIibs7X1xeACRMmEBwcTHh4OCNHjmTDhg2kpaUZXJ1IyWW1Whm3eC8Hz16kbJAvsx93vWasq1mtVuLS4ohLi8Nqtdr9/AquIiJurlatWlgsFrKzs3Mfs1gsAMXyg0VECueTn06wdHccXp4efPBoEyqVdr1mLEdTcBURcXMtW7akSpUqjB8/nrS0NJKSknj33Xfp0KFDbrOWiDjWlmOJvL7C1ow1ofOd/K2mmrEKQ8FVRMTN+fj4MG/ePLy8vOjYsSMdO3akYsWKvP7660aXJlIinUnOYPgXuzBbrDwUVZmBraoZXZLL0O2wRERKgAoVKvDuu+8aXYZIiZeZbeaZeTtISjNRv1Ip3ujZ0KWbsRxNM64iIiIiDmC1Whm/ZB/7zqQQFuTLHBfcGctomnEVERERcYBPt5xg8c4zfzVjRXF7mUCjS7I7Dw8PaobWzH3f3hRcRURERIrZz8fPM/k7WzPWuE51aVkz3OCKikeAdwBLeywttvNrqYCIiIhIMYpLzmDY5zsxW6x0j6zE/7WubnRJLkvBVURERKSYZGabGTJ/B+fTTNS7rRRv9mykZqxboOAqIiIiUgysViv/XLqfPX+kUDrQhzn9mxLg697NWBk5GfRY2oMeS3sUy5avWuMqIiIiUgzm/3ySmB1/4OkBH/RrQpUw92vGuprVauVYyrHc9+1NM64iIiIidrb99yQmfXsQgLGd6tI6wj2bsRxNwVVERETEjs6mZDD0853kWKx0bVyJp9rUMLokt6HgKiIiImInWTlmnpm/k8TULOpWDGFKL+2MZU8KriIiIiJ2YLVaeWnpAfacTiY0wIcP+0cT6Kt2IntScBURERGxg8+3nWJh7Gk8PWB6vyjuKOv+zViOpl8DRERERG5R7IkkJn17AIAxD9Slbe1yBldkDA8PDyoFVcp9394UXEVERERuQfzFTIZ8vpNss5UHG97G4LYltxkrwDuA1b1XF9v5tVRARERE5CZl5dh2xkq4lEWdCiH8u7d2xipOCq4iIiIiN+lf3xxk56lkSvl7M6d/U4L89Mfs4mRIcD106BADBw6kefPmtGrVijFjxpCUlJTv2EGDBtGwYUOioqJy3zZu3OjgikVERETy+mLbKRZsP4WHB0zrF0W18CCjSzJcZk4mjyx/hEeWP0JmTqbdz+/w4JqZmcmgQYOIiopi8+bNLF++nOTkZMaPH5/v+P379/Pxxx+za9eu3Le2bds6uGoRERGR/9lx8gIvf7MfgBfur8PddcobXJFzsFgtHDh/gAPnD2CxWux+focH17i4OOrWrcuwYcPw9fWlTJky9O3bl19++eWasadPnyYlJYV69eo5ukwRERGRfJ27mMmQ+TvINlvp1KAiQ++uaXRJJYbDg2uNGjWYO3cuXl5euY+tXr2a+vXrXzN23759BAUFMWrUKO666y66dOlCTEyMI8sVERERyWXKsTDk852cu5RFRPlg3urTWM1YDmToCmKr1cp7773HunXrmD9//jXPm0wmIiMjGTVqFBEREWzbto0RI0YQFBREp06dDKhYRERESrJXlh9gx8kLhPh78+ET0QSrGcuhDPvXTk1NZdy4cRw4cID58+dTp06da8b06NGDHj165B63bt2aHj16sHLlSgVXERERcaiFv5xi/s+2Zqz3H4mkupqxHM6QuwqcOnWKXr16kZqaSkxMTL6hFSAmJoaVK1fmecxkMuHn5+eIMkVEREQA2HXqAhOX2nbGer5Dbe6pW8HgikomhwfXlJQUBgwYQJMmTfj4448JCwsrcGxqaiqTJ0/m4MGDWCwW1q9fz/Lly+nbt68DKxYREZGS7NylTIbM34nJbOH+ehUY1r6W0SU5tTJ+ZSjjV6ZYzu3wpQKLFy8mLi6OlStXsmrVqjzP7dq1i6ioKCZNmkS3bt0YMGAA6enpDB8+nPPnz1OlShWmTJlCdHS0o8sWERGREsiUY2HY5zv582ImNcsF8fbDjfH0VDNWQQJ9Atn4SPHdb9/DarVai+3sTsBsNrN7924iIyPz3MlAREQKR9dRKcleWrafz7aeJMTPm6XDW1GzXLDRJZVo2vJVREREJB+LYk/z2daTALzbN1Kh1QkouIqIiIhcZc/pZP651LYz1qgOtelQT81YhZGZk8nAVQMZuGpgsWz5qpuPiYiIiFwh4VIWg+ftwJRjocOdFRhxj5qxCstitRAbH5v7vr1pxlVERETkL9lmC8O+sDVj1SgXxDt91YzlTBRcRURERP7y2ne/sv33JIL9vPmwfzSl/H2MLkmuoOAqIiIiAny94w/+u+UEAO883Jha5dWM5WwUXEVERKTE2/dHCuOW7APg2XsjuL9+RYMrkvwouIqIiEiJdj41i8HzYjHlWLi3bnlG3hthdElSAN1VQEREREqsy81YcSmZ1AgP4t1HItWMdYsCvAOK7dwKriIiIlJivbHiED8fTyLI14s5/ZuqGesWBfoEsv2x7cV2fi0VEBERkRJpya4/+OSn3wF4++HGRFQIMbgiuREFVxERESlx9p9JYezXtmas4e1r8UCD2wyuSApDwVVERERKlKQ0E4Pn7SArx0L7OuUYdV9to0tyG1nmLIauHcrQtUPJMmfZ/fxa4yoiIiIlRo7ZwogFOzmTnEG1soG890gUXmrGshuzxcymM5ty38fLvufXjKuIiIiUGFNWHeKno+cJ9PXiwyeiCQ1QM5YrUXAVERGREmHZ7jN8tOmvZqw+jamtZiz7s1j+9/6mTWA22/X0Cq4iIiXAihUrqFevHlFRUblvo0ePNrosEYc5GHeRF7/eC8DQu2vSqaGasexu8WK4887/HXfqBNWq2R63E61xFREpAfbt20f37t154403jC5FxOEupJkYPD+WzGwLbWuX4x/31zG6JPezeDH07g0+AKX/9/iZM7bHY2KgZ89bfhnNuIqIlAD79u2jQYMGRpch4nA5ZgvPfrmL00kZ3BEWyHQ1Y9mf2QzPPQdW67XPXX5s5Ei7LBtQcBURcXMWi4UDBw6wfv162rdvT9u2bZk4cSIpKSlGlyZS7N5ac5hNRxIJ8PHiwyeaEhqoZiy7+24R/PFHwc9brXD6tG3N6y1ScBURcXNJSUnUq1ePjh07smLFCr788ktOnDihNa7i9pbvjWPOhuMAvNWnEXUrljK4IjdjtcL2j2D+0NyHAk1W9j25n31P7ifQdNUM7Nmzt/ySWuMqIuLmwsPD+fzzz3OPAwICGD16NA8//DCpqakEBwcbWJ1I8Tj050VGf2VrxhrcrgZdGlUyuCI3k3wKlg2H3zdAYE7hPua2W2+I04yriIibO3ToEFOnTsV6xfozk8mEp6cnvr6+BlYmUjyS0008/dkOMrLNtIkIZ0zHukaX5D6sVtjxKcxsaQut3gHw1L/h9tvBo4C1wx4eUKUKtGlzyy+vGVcRETdXunRpPv/8c0JDQxk4cCDnzp3jrbfe4qGHHlJwFbdjtlh59svdnEpKp0pYANPUjGU/F+Pgm2fh6Pe24yotoPtMCK8F799mu3uAh0feJq3LYfa998Dr1rfR0oyriIibq1ixInPmzOGHH36gefPm9OrVi4YNG/LSSy8ZXZqI3b295jAbf0vA38eTOY9HUyZIv5zdMqsVdi+AGXfZQquXH9w3GQautIVWsN3qKiYGKlfO+7G33263W2EBeFit+d27wH2YzWZ2795NZGQkXnZI+iIiJY2uo+IqVuw7y9DPdwLw/iORdI+sfIOPkBu6FA/LR8LhFbbjSk3godlQroB74ZrNtrsHnD1rW9Papo1dZlov01IBERERcXmH/7zEC1/tAeCpNtUVWm+V1Qr7v4YVL0DGBfD0gfbjoOVz4HWd+OjlBXffXWxlKbiKiIiIS0tJz+bpebGkm8y0qlWWFx9QM9YtSUuE756Hg8tsxxUb2WZZK9Q3ti4UXEVERMSFmS1Wnlu4i5Pn06lcOoDp/Zrg7aUWnpt28BtYPgrSE8HTG9qOgTbPg5dzbNyg4CoiIiIu693vf2P94b+asfo3JUzNWDcnPQlWjIb9Mbbj8vXhoVlwW2Nj67qKgquIiIi4pFX7z/LBuqMATOnViAaVQw2uyEUdXgnfPgep8eDhBa1HQbsXwdv5fglQcBURERGXcyT+Ev9YZGvG+r/Wasa6KRnJsGoc7PnCdhxexzbLWrmpoWVdj4KriIiIuJSLmdk8PW8HaSYzd9UIY1wnNWMV2ZG18M0IuBQHeEDLEdB+Avj4G13ZdSm4ioiIiMuwWKyM+nI3vyemUbl0ADMeVTNWkWRehDUTYOdntuOwmtBjFtzRwti6CknBVURERFzGez8c4YdD5/DztjVjlQ32M7ok13F8PSwbDimnAQ+4awjcMxF8A42urNAUXEVERMQlrDnwJ9N+OALA6w81VDNWYWWlwtqX4Ze5tuMy1aD7TKjWytCyboYhc+uHDh1i4MCBNG/enFatWjFmzBiSkpLyHbthwwa6du1KZGQknTp1Yt26dQ6uVkRERIx29Fwqz//VjPVky2r0anq7wRW5iBM/wexW/wutzQbBMz+5ZGgFA4JrZmYmgwYNIioqis2bN7N8+XKSk5MZP378NWNPnDjBiBEjeO6554iNjWXEiBGMHDmS+Ph4R5ctIiIiBrmUadsZKzUrhxbVw5jw4J1Gl+T8TOmwciz890G4cAJCq8ATy+DBt8Ev2OjqbprDg2tcXBx169Zl2LBh+Pr6UqZMGfr27csvv/xyzdglS5YQHR1Nhw4d8Pb2pnPnzjRr1oyFCxc6umwRERExgMViZdTCPRxPSOO2UH9mPNYEHzVjXd/p7TC7NWybBVihyRMwZAvUuNvoym6Zw9e41qhRg7lz5+Z5bPXq1dSvf+3+t0ePHqV27dp5HqtVqxaHDh0q1hpFRETEOUz/8Shrf43H19uT2Y83JVzNWAXLzoR1r8HWD8BqgZBK0G06RHQwujK7MbQ5y2q18t5777Fu3Trmz59/zfNpaWkEBATkeczf35/09HRHlSgiIiIGWXswnnfX/gbAaz0a0LhKaWMLcmZndsCSIZB42Hbc+FF44A0IKG1oWfZmWHBNTU1l3LhxHDhwgPnz51OnTp1rxgQEBJCZmZnnsczMTIKCghxVpoiIiBjgWEIqoxbuBuCJv1WlT3QVYwtyVjlZsGEKbH4PrGYIrgBd3oO6nY2urFgYElxPnTrFU089RaVKlYiJiSEsLCzfcbVr1+bAgQN5Hjt69CgNGjRwRJkiIiJigEuZ2Tz9WSyXsnJoXi2MiV3qGV2Sczq7xzbLeu6vrNSwD3T6NwTmn6vcgcNXN6ekpDBgwACaNGnCxx9/XGBoBejWrRvbt29nxYoV5OTksGLFCrZv30737t0dWLGIiIg4isVi5R+L9nAsIY2KpdSMlS9zNqx/Ez66xxZaA8Ph4c+g11y3Dq1gwIzr4sWLiYuLY+XKlaxatSrPc7t27SIqKopJkybRrVs3atasyYwZM5g6dSoTJkygcuXKTJ8+nerVqzu6bBEREXGAGeuOsuZgPL5ensx6vAnlQtSMlUf8AVjyDPy513Zcrzs8+A4EhRtbl4N4WK1Wq9FFFCez2czu3buJjIzEy8vL6HJERFyOrqPiKD8eiuf/Po3FaoUpvRrSt9kdRpfkPMw5sOV9WPcGWLIhoAx0ngoNeoGHh9HVOYy2fBURERHD/Z6YxnNf7sZqhcfvukOh9UoJh2HpENudAwDqdLY1YIVUMLQsIyi4ioiIiKFSs3JszViZOURXLcNLXa69t3uJZDHD1hnw46tgzgK/UOj8b2jUt0TNsl6pSKudx44dm+8OVyIiUjx03RV3Z7VaeWHRHo6cS6V8iB8zH2+Cr7easTh/DP7TCb6faAutte6DYT9D40dKbGiFIs64BgYGMmLECEJCQnjooYfo2bMnFStWLK7aRERKPF13xd3NXH+MVQf+xMfLg9n9m1I+xN/okoxlscD2D2HtvyAnA3xD4IHXIap/iQ6slxW5OSs7O5t169axZMkSfvrpJ5o1a0avXr3o0KEDvr6+xVXnTVNTgYi4OqOvu7qOSnFZd/gcf//vL1it8EbPhvRrXsLXtSb9DsuGwcmfbMc17oZuH0Bpbb5w2S3dVWD37t288sorHDx4kNDQUHr27MnQoUMJCQmxZ423RBdcEXEnRlx3dR2V4nAiMY1uH2zmYmYO/ZrfwRs9GxpdknEsFtjxCax5CbLTwCcI7p8M0X/XLOtViryIJCEhgf/85z/06NGD/v37U6lSJWbOnMmnn37K77//zpAhQ4qjThGREkvXXXE3aVk5DJ63g4uZOTS5ozT/6laCd8ZKPgXzesB3/7CF1qqtYchP0Oz/FFrzUaQ1rv/3f//Hzz//TI0aNejZsyfdu3fPs/PV888/T9++fe1epIhISaXrrrgbq9XK6Jg9HI6/RLkQP2Y93hQ/7xI4k2+1ws7PYPUEMF0C7wDo8C9o/jR4qjmtIEUKrrfffjsLFiygUaNG+T5fuXJlYmJi7FKYiIjouivuZ/aG46zY91cz1uNNqFCqBDZjpZyBb5+Fo2ttx1VaQPeZEF7L2LpcgHbOEhGR69J1VOxlw28JPPmf7Vit8NpDDXisRVWjS3IsqxX2fAkrX4SsFPDyg3v+CX8bBp763ioMbUAgIiIixe7k+TSeXbALqxUeaVaFR0vaHQQuxcO3z8FvK23HlZpAj1lQvq6xdbkYBVcREREpVukmWzNWSkY2kVVKM6l7fTxKSuOR1Qr7v4YVL0DGBfD0gbvHQquR4KUYVlT6FxMREZFiY7VaGROzl0N/XiI82I/ZJakZKy0Rlo+CX7+xHVdsBA/Nhgra0vZmKbiKiIhIsflo03GW7z2Lt6cHsx5vQsXQEtKMdXAZLH8e0hPB0xvavABtXwAvH6Mrc2kKriIiIlIsNh1J4M2VhwB4uWs9mlULu8FHuIH0JFgxGvb/dbeP8vWhx0yoFGloWe5CwVVERETs7nRSOiMW7MJihT5Nb+fxu0rAHQQOrbA1YKWdAw8vaD0K2o0Bbz+jK3MbCq4iIiJiVxkmM0/P20FyejaNbw9lco8G7t2MlXEBVo2DPQtsx+F14KFZULmpsXW5IQVXERERsRur1cqLX+/l17MXCQ/2ZdbjTfH3ceNmrCPfwzcj4NJZwANajoD2E8CnhKzldTAFVxEREbGbjzf/zjd74vD29GDGo02oVDrA6JKKR+ZFWD0eds2zHYfVtN2X9Y4Wxtbl5hRcRURExC62HE3k9RW/AvDPB++kRY2yBldUTI6vh2XDIeU04AF3DYF7JoJvoNGVuT0FVxEREbllf1xIZ9gXO7FYoWeTygxoWc3okuwvKxW+fwliP7Ydl6kG3WdCtVaGllWSKLiKiIjILckwmRk8bwcX0rNpWDmU1x9q6H7NWCc2w9KhkHzSdtxsEHSYBH7BxtZVwii4ioiUEGazmSeffJLKlSvz5ptvGl2OuAmr1cq4xXs5EHeRsCBfZvd3s2YsUzr88Apsm2U7Dq0C3aZDzfbG1lVCKbiKiJQQH3zwAbGxsVSuXNnoUsSN/OenEyzdHYeXpwcfPBpFZXdqxjq1DZYOgaRjtuMmT8D9r4F/KWPrKsEUXEVESoCtW7eyZs0a7r//fqNLETey9dh5XvurGWt85ztpWTPc4IrsJDsT1r0GWz8AqwVCKtlmWSM6GF1ZiedpdAEiIlK8zp8/z4QJE3j77bcJCHCj2TAx1JnkDIZ/sROzxcpDUZX5e6tqRpdkH2d2wJy2sGWaLbQ27gdDtyq0OgnNuIqIuDGLxcLo0aMZOHAgdevWNboccROZ2WaembeD82km6lcq5R7NWDlZsGEKbH4PrGYIKg9d34e6nY2uTK6g4Coi4sbmzJmDr68v/fv3N7oUcRNWq5UJS/az70wKZQJ9mP14UwJ8XbwZ6+weWDIEzh2wHTfoDZ3fgsAwY+uSayi4ioi4sWXLlnHu3Dmio6MByMzMBGDt2rXExsYaWZq4qM+2nuTrnX/g6QEfPNqEKmEufNN9czZsehs2vgWWHAgsC13ehXrdja5MCqDgKiLixlatWpXneOzYsQC6HZbclG3HzzN5+UEAxnW6k1a1XLgZK/4ALHkG/txrO76zGzz4DgSXM7YuuS4FVxEREbmhuOQMhn2xkxyLlW6NKzGoTXWjS7o55hzY8j6sewMs2RBQBjpPhQa9wNXX6ZYACq4iIiWIZlrlZmRmmxkyfweJqSbuvK0UU3o1cs1mrITDtvuyntlhO67dydaAFVLB2Lqk0BRcRUREpEBWq5WJS/ez548USgf68GF/F2zGsphh6wz48VUwZ4FfKHT+NzTqq1lWF6PgKiIiIgWa//NJvtpha8aa3i/K9Zqxzh+zzbKe3mY7rtXBtplAqUrG1iU3RcFVRERE8vXLiSQmfWtrxnrxgbq0iXChxiWLBbbPgbWTICcDfEPggdchqr9mWV2YgquIiIhc48+UTIbMtzVjPdjoNp5uW8Pokgov6XdYNhxObrYdV28H3T+A0ncYW5fcMgVXERERySMrx8wz83eQmJpF3YohvNXbRZqxrFaI/QTWTITsNPAJgvsmQbNBmmV1E55GvnhSUhL33Xcf27ZtK3DMoEGDaNiwIVFRUblvGzdudGCVIiIiJYfVauXlZQfYfTqZ0AAf5vRvSqCvC8xzJZ+GeT3gu+dtobVqaxjyEzR/SqHVjRj2lbhjxw7Gjh3LqVOnrjtu//79fPzxxzRv3txBlYmIiJRcX2w/xZe/nMbDA6b1i6Jq2SCjS7o+qxV2zYNV48F0CbwDoMO/oPnT4Gno/JwUA0P+R5csWcILL7zAqFGjrjvu9OnTpKSkUK9ePQdVJiIiUnLtOJnEv745AMDojnVoV9vJm7EuxsHnfeCbEbbQWqUFPLMZ7npGodVNGfK/2rp1a77//ns6d+583XH79u0jKCiIUaNGcdddd9GlSxdiYmIcVKWIiEjJEX8xk2fm7yTbbKVzw4oMaVfT6JIKZrXC7gUw4y44+j14+cF9k2HgSgivZXR1UowMWSpQrlzhfoMzmUxERkYyatQoIiIi2LZtGyNGjCAoKIhOnToVc5UiIiIlQ1aObWeshEtZ1K4QzFu9GztvM9aleFg+Eg6vsB1XagI9ZkH5uoaWJY7h1Kute/ToQY8ePXKPW7duTY8ePVi5cqWCq4iIiJ1M+vYgO08lU8rfmw/7RxPk54TxwGqF/V/Dihcg4wJ4+sDdY6HVSPBywnqlWDj1/3RMTMw1s6smkwk/Pz8DqxIREXEfC7af4ottp/DwgPf7RVEt3AmbsdISbXcLOLjMdlyxETw0GyrUN7YucTinDq6pqam88847VK1albp167Jx40aWL1/Oxx9/bHRpIiIiLm/nqQu8vMzWjPXC/XVoX6e8wRXl4+A3sHwUpCeCpze0HQ1t/gFePkZXJgZwuuAaFRXFpEmT6NatGwMGDCA9PZ3hw4dz/vx5qlSpwpQpU4iOjja6TBEREZd27lImQ+bvwGS28ED9igy928masdKTYMVo2P9XU3b5+tBjJlSKNLQsMZaH1Wq1Gl1EcTKbzezevZvIyEi8vLyMLkdExOXoOup+TDkWHv3oZ2JPXiCifDBLhrUi2JnWtR5eCd8+B6nx4OEFrUdBuzHgraWCJZ0TfZWKiIiII0xefpDYkxcI8ffmwyeinSe0ZiTDqnGw5wvbcXgdeGgWVG5qaFniPJzkK1VEREQcYdEvp5n380k8POC9vpFUd5ZmrCNrbRsJXIoDPKDlcGj/T/DxN7oycSIKriIiIiXE7tPJ/HPpfgBGdajNvXdWMLgiIPMirJkAOz+zHYfVtK1lveMuY+sSp6TgKiIiUgIkXMrimXm2Zqz761VgeHsn2GHq+HpYNhxSTtuOWwyBe18C30BDyxLnpeAqIiLi5rLNFoZ9vpM/L2ZSs1wQbz/cGE9PA3fGykqF71+C2L9ub1m6qm2WtVpr42oSl6DgKiIi4uZeXX6Q7SeSCPazNWOF+Bt4D9QTP8GyoXDhhO242SDoMAn8go2rSVyGgquIiIgb+yr2NJ9uPQnAu30jqVnOoIBoSocfXoFtswErhFaBbtOhZntj6hGXpOAqIiLipvb+kcyEv5qxRnaI4L56BjVjndoGS4dA0jHbcZMn4P7XwL+UMfWIy1JwFRERcUOJqX81Y+VY6HBneZ69J8LxRWRnwrrXYOsHYLVASCXoNg0i7nN8LeIWFFxFRETczOVmrLiUTGqEB/FO30jHN2Od2QFLhkDiYdtx437wwJsQUNqxdYhbUXAVERFxM6+v+JVtv19uxmpKKUc2Y+VkwYZ/w+Z3wWqGoPLQ9X2o29lxNYjbUnAVERFxI4t3/sF/fjoBwNsPN6ZW+RDHvfjZPbB0KMTb1tXSoDd0fgsCwxxXg7g1BVcRERE3sf9MCuMW7wPg2Xtq0bF+Rce8sDkbNr0NG98CSw4EloUu70K97o55fSkxFFxFRETcwPnULAbP20FWjoX2dcoxskNtx7xw/EFY+oxtthXgzm7w4DsQXM4xry8lioKriIiIi8sxWxj+xS7OJGdQrWwg7z0SVfzNWOYc2PI+rH8TzCbwLw0Pvg0NeoGHgbtyiVtTcBUREXFxb6w8xNbj5wny9eKjJ6IJDSjmZqyEw7b7sp7ZYTuu3Qm6vgchDlqaICWWgquIiIgLW7rrDB9v/h2wNWNFVCjGZiyLGX6eCT9MBnMW+IVCpzdtt7rSLKs4gIKriIiIi9p/JoWxi/cCMKx9TR5ocFvxvdj5Y7Y7Bpz+2XZcqwN0nQahlYvvNUWuouAqIiLigpLSTAyet4PMbAt31ynH8/fVKZ4Xslhg+4ew9l+QkwG+IdDxNdu2rZplFQdTcBUREXExOWYLIxbs5ExyBlXLBvJ+3yi8iqMZK+l3WDYcTm62HVdvC91nQOk77P9aIoWg4CoiIuJi/r36MD8dPU+grxcf9o8mNNDOzVhWK8R+AmsmQnYa+ATB/a9A07+Dp6d9X0ukCBRcRUREXMg3e+L4cONxAN7q3Zg6Fe3cjJV8Gr4ZDsfX246rtobuH0BYdfu+jshNUHAVERFxEQfjLjImxnaj/2fa1eTBRnZsxrJaYdc8WDUeTJfAOwA6/AuaP61ZVnEaCq4iIiIu4EKaicHzY8nMttAmIpzRHe3YjHUxDr59Do6ssR1XaQHdZ0J4Lfu9hogdKLiKiIg4ObPFyrNf7uJ0UgZVwgKY3s9OzVhWK+xdCCvHQGYKePnBPf+Evw0DT69bP7+InSm4ioiIOLm3Vh9m05FEAnxszVilA31v/aSX4mH5KDj8ne24UhPoMQvK1731c4sUEwVXEZESYOvWrbzzzjscO3aMgIAAHnjgAUaPHo2/v7/RpckNLN8bx+wNxwD4d+9G3HlbqVs7odUKBxbDd/+AjAvg6QN3j4VWI8FLsUCcm1Zbi4i4uaSkJAYPHky/fv2IjY1lyZIlbN++nQ8//NDo0uQGDv15kdFf2XbGGty2Bl0bV7q1E6YlwlcDIObvttBasREM3gBtX1BoFZegr1IRETcXFhbGli1bCA4Oxmq1kpycTFZWFmFhYUaXJteRnG7i6c92kJFtpnUtOzRjHfzGtjQgPRE8vaHNC38FVjvfA1akGCm4ioiUAMHBwQC0a9eO+Ph4oqOj6dmzp8FVSUHMFivPfbmbU0np3F7G1ozl7XWTfyRNT7I1X+37ynZcvp5tLWulSLvVK+IoWiogIlKCrFmzho0bN+Lp6cmzzz5rdDlSgLfXHGbDbwn4+3gyp39TygTdZDPW4VUw8y5baPXwhDb/gKfXK7SKy9KMq4hICeLv74+/vz+jR4+mT58+pKSkEBoaanRZcoUV+84yc72tGWtKr0bUr3QT/z8ZybBqHOz5wnYcXht6zIbbm9qvUBEDaMZVRMTN7dy5kwceeACTyZT7mMlkwsfHh4CAAAMrk6v9Fn+JF76y7Yw1qHV1ukdWLvpJjq6FmX/7K7R6QMsRMHiTQqu4BQVXERE3V6dOHTIzM3n77bcxmUycOXOGKVOm0Lt3b3x97XA/ULGLlIxsnv4slnSTmZY1yzK2UxHvp5p1Cb55Fub3gktxEFYT/r4a7n8VfHTbM3EPWiogIuLmgoKCmDt3Lq+//jqtWrUiJCSErl27MmzYMKNLk7+YLVZGfrmLE+fTqVw6gA8ebVK0ZqzjG2DZcEg5ZTtuMQTufQl8A4unYBGDKLiKiJQAtWrV4pNPPjG6DCnAe2t/Y93hBPy8bc1YYYVtxspKhbX/gl8+sh2Xrgo9ZkK11sVWq4iRDF0qkJSUxH333ce2bdsKHLNhwwa6du1KZGQknTp1Yt26dQ6sUEREJH8p6SaOnUtl16kLHEtIJSXddOMPyseq/X8y/cejALzZqyENKheyGevETzC71f9Ca7NBMGTLTYVWe30uIsXNsBnXHTt2MHbsWE6dOlXgmBMnTjBixAjeeecd7r77btasWcPIkSNZs2YNFSpUcGC1IiIi/xOXnMGLX+9l05HE3MfaRoTzZq9GVCpd+Ia3o+cu8Y9FuwH4e6vqPBR1+40/yJQOP06Gn2cBVgitAt2mQ832RfwsbOz1uYg4giHBdcmSJUybNo3Ro0czatSo646Ljo6mQ4cOAHTu3JnFixezcOHCIt9/0GQy4eXldc3jnp6eeHt75xlXEA8PD3x8fG5qbHZ2Nlar1aFjgTyNF0UZm5OTg8VisctYHx8fPDw8inWs2WzGbDbbZay3tzeenp5OM9ZisZCTk1PgWC8vr9yvbWcYa7Vayc7OtsvYK78/i2ssXP97uTBj1eBUsqSkm64JegAbjyQy9uu9TO8XRWjgjb8mLmZm8/RnO0gzmbmrRhjjOheiGev0dlg6BM7bZmhp8gTc/xr4l7qZT8Vun4uIoxgSXFu3bk3Xrl3x9va+bnA9evQotWvXzvNYrVq1OHToUJFf85133sn3B29ERASPPvpo7vHUqVML/IFXtWpVnnzyydzj999/n/T09HzHVqpUiaeeeir3eMaMGaSkpOQ7tly5cgwdOjT3+KOPPiIhISHfsaGhoYwcOTL3+L///S9xcXH5jg0MDGT06NG5x59//jknT57Md6yPjw/jx4/PPV60aBFHjhzJdyzAyy+/nPv+kiVLOHjwYIFjx40bl/uDffny5ezZs6fAsS+88AJBQUEArF69mtjY2ALHPvfcc5QuXRqAH374ga1btxY4dsiQIZQvXx6ATZs2sWHDhgLHDho0iMqVbbeg+fnnn1m7dm2BYwcMGEC1atUA218RVq5cWeDYfv365X4979u3j2XLlhU4tnfv3tSvXx+AX3/9lZiYmALHdu/encjISMD2PbNgwYICx3bq1InmzZsDcOrUKT799NMCx3bo0IFWrVoBcPbsWebOnVvg2Hbt2nH33XcDkJCQwKxZswoc+7e//Y37778fgJSUFN5///0Cx0ZHR/Pggw8CkJ6eztSpUwsc27hxY3r06AHYfkl74403Chxbr149+vTpk3t8vbGFuUZc+f0g7i8x1XRN0Lts45FEElNNNwx7FouVUV/u5nhiGpVC/ZnxaBN8rteMlZ0J61+HLdPBaoGQ22yzrBH33cqnYpfPRcSRDAmu5cqVK9S4tLS0a+4x6O/vX2BYFBERKW4XMwuezQe4dIPnAd7/4Qg/HDqHr7cnc/pHUzbYr+DBZ3baZlkT/pq0adwPHngDAsoUpex82eNzEXEkD+v1/nbsAHXq1OGzzz6jRYsW1zw3ZMgQqlWrxosvvpj72Jtvvsnp06eZMWNGoc5vNpvZvXs39erV01IBLRUo1FgtFbi1sVoq4H4uX0cjIyPzvY6WNMfOpXLvOwX/1eaH59tRs3xwgc9/fzCepz6z/TXp7T6N6dW0gHWtOVmw4d+w+V2wmiGoPHR9D+o+eCvl53Grn4uIozn17bBq167NgQMH8jx29OhRGjRoUORz+fr6FuqCW5QfQEUZe2XYdIWxV/6gdoWxV4Yhdxvr6elZ6K81Zxjr4eHhUmOh+L7vxT2FB/vSNiKcjfn8ib1tRDjhwQV/jRw9l8qohbsBeLJltYJD69k9sGQInPvrZ2D9nvDg2xAYdqvl53Ern4uIEZx656xu3bqxfft2VqxYQU5ODitWrGD79u10797d6NJERKSECg305c1ejWgbEZ7n8bYR4Uzp1ajANaGXMrN5el4sqVk5NK8exoQH77x2kDkb1r8JH91jC62BZaHPp9DnP3YPrbfyuYgYxelmXKOiopg0aRLdunWjZs2azJgxg6lTpzJhwgQqV67M9OnTqV69utFliohICVapdADT+0WRmGriUmY2If4+hAf7Fhj0LBYrzy/aw/GENG4L9WfmY/k0Y8UfgCXPwJ97bcd3doUH34XgwvWFOOpzETGS4cH18OHDeY537dqV57hNmza0adPGkSWJiIjcUGhg4cPd9B+P8v3BeHy9PZn9eFPCr2zGMufAlvdh3RtgyQb/0rZlAQ16wV9r84tbUT4XESMZHlxFRETc2Q+/xvPu2t8AeK1HAxpXKf2/JxMO2+4YcGaH7bh2J1sDVkhFh9cp4goUXEVERAqQkm4iMdXExcxsSgX4EB5UtJnJ4wmpjPxyNwD976pKn+gqticsZtg6A358FcxZ4BcKnd603erqFmZZb7VeEWen4CoiIpKPW90KNTUrh6fn7eBSVg7NqpVhYpd6tifOH7PNsp7eZjuuea9tM4HQyobWK+IKnPquAiIiIka40VaoKekF3/sXbM1Y/1i0m6PnUqlYyp8ZjzXB1xP4eRbMamULrb4h0PV9ePzrWw6tt1qviKvQjKuIiMhVbnUr1Jnrj7L6QDy+Xp7MerwJ5bPPwqfD4eRm24DqbaH7DCh9h1PUK+IqFFxFRESucitboa47dI63v7c1Y03uXo+o+MUwbyJkp4FPINz3CkT/H3ja74+e2rpVSgoFVxERkauU8r/+ToMhBTx/IjGNZ7/chdUKQ6N86XvoWTi+3vZk1Va2WdYw+9+L/GbrFXE1WuMqIiJylctboeanoK1Qbc1YsVzKzGZM+e2MPjbQFlq9A+CBN2HA8mIJrTdbr4grUnAVERG5SlG3QrVarYz+ag8p8af4PGAqQy++h4fpEtzeHJ7ZDHcNsevSgFutV8RVaamAiIhIPoqyFeqs9UfxP/gVa/w+I9SaBl5+cM8E+Ntw8PRyunpFXJWCq4iISAEKsxXqlt0HqPXjCIb6/rX7VaUo6DEbytd1QIV5aetWcXcKriIiIjfDaiXh5y+ot3oMpb1SyfHwxrv9OGg1Ery8tYuVSDFQcBURESmqtERyvh1FuUPfAHDcuwa3D/wUKjcCtIuVSHFRc5aIiEhRHPwG64wWeB/6hmyrFx96PkzQsI34/hVatYuVSPFRcBURESmM9CSI+T9Y1B+P9EQOWarQO2cyTZ6YQoUyIbnDCrOLlYjcHC0VEBERuZHDK+Hb5yA1HquHJzOzu/J+Tk9e6hFFdLWwPEO1i5VI8VFwFRERKUBKUgIeq8dR6vBXAGSVrsnfk/+Pn3Kq0Te6Co+1uOOaj9EuViLFR8FVREQkH+d3f4fHt88SZk7EYvXgI3NnZic9wgWTF42rlGZS9/p4eHhc83GXd7HamM9yAe1iJXJrtMZVRETkSlmXMC0eTtmljxJmTuR3SwX6mF7ijZzHuGDywsfLg7d6NcTfJ/+NBbSLlUjx0YyriIjIZcc3wLLh+KacAuA/OR2ZkvMImfjlDsk2W/G6wfat2sVKpHgouIqIiGSlwtp/wS8f2Q6Dq/DE+SfZZr0z3+GFabDSLlYi9qelAiIiUrKd3AKzW+WGVqL/jzP9figwtIIarESMohlXEREpmbIz4IdX4OdZgBVCq0C36VCzPWXTTbSqWZafjp2/5sPUYCViHAVXEREpeU5vh6VD4PxR23FUf+j4OviXAqBUgA9B/tf+iFSDlYixFFxFRKTkyM6E9a/DlulgtUDIbdB1GtS+P8+wT346wZoD8Xh5ePBW70bUKBekBisRJ6DgKiIiJcOZHbBkCCQeth037gcPvAEBZfIM23IskddX/ArAP7vcSc+mtzu6UhEpgIKriIi4txwTbJgCm98FqxmCykPX96Dug9cMPZOcwfAvdmG2WOnZpDJPtqzm8HJFpGAKriIi4r7O7rWtZY3fbztu0Bs6vwWBYdcMzcw2M+jTX0hKM1GjXBCDWlfnYka2lgaIOBEFVxERcT/mbNj0Dmz8N1hyILAsPPgO1O+R73Cr1cqohbv59ewlAI4npNF52mbaRoTzZq9GVCod4MDiRaQguo+riIi4l/iDMPdeWxOWJQfu7ApDtxUYWgFmrT/Gyv1/XvP4xiOJjP16LynppmIsWEQKSzOuIiLiHsw5sOV9WP8mmE3gXxoefBsa9AIPjwI/7Ofj55m65nCBz288kkhiqklLBkScgIKriIi4voTfYOkztjsHANTuZGvACql43Q+LS85g2Oc7sVivf/rCbPEqIsVPSwVEREqAQ4cOMXDgQJo3b06rVq0YM2YMSUlJRpd16yxm2z1ZZ7e2hVa/UOgxC/otuGFozcw288z8HZxPM1GrXPB1x2qLVxHnoOAqIuLmMjMzGTRoEFFRUWzevJnly5eTnJzM+PHjjS7t1pw/Bv/pDGv+CeYsqHkvDN0KkY9ed2kA2Jqx/rl0P3v/SKF0oA/T+kXSNiI837Ha4lXEeSi4ioi4ubi4OOrWrcuwYcPw9fWlTJky9O3bl19++cXo0m6OxQLb5sCsVnD6Z/ANse1+9fjXEFq5UKeY9/NJYnb8gacHfNCvCfUqhfJmr0bXhFdt8SriXLTGVUTEzdWoUYO5c+fmeWz16tXUr1/foIpuwYUTsGw4nNhkO67eFrrPgNJ3FPoU246f55VvDwIwtlNdWv8VViuVDmB6vygSU01cyszWFq8iTsiQ4Hr+/HkmTpzI9u3b8fLyolu3brz44ot4e19bzqBBg9i2bVue595//33atm3ryJJFRNyC1WrlvffeY926dcyfP9/ocgrPaoXYT2DNRMhOA59AuO8ViP4/8Cz8Hw/PpmQw7Iud5FisdG1ciafa1MjzfGiggqqIMzMkuI4cOZIKFSqwadMmEhMTGTJkCP/9738ZNGjQNWP379/Pxx9/TPPmzQ2oVETEfaSmpjJu3DgOHDjA/PnzqVOnjtElFU7yafhmOBxfbzuu2so2yxpWPXdISrqJxFQTFzOzKRXgQ3jQtQHU1oy1k8RUE3UrhjClV0M8brAWVkSci8OD68mTJ9m+fTsbN24kICCAKlWqMHToUN56661rguvp06dJSUmhXr16ji5TRMStnDp1iqeeeopKlSoRExNDWNi1W546HasVds2H1eMh6yJ4B8C9L0GLZ/LMssYlZ/Di13vZdCQx97Grd7yyWq28tGw/e04nExrgw4f9own01Wo5EVfj8OasI0eOULp0aSpUqJD7WM2aNYmLi+PixYt5xu7bt4+goCBGjRrFXXfdRZcuXYiJiXF0ySIiLi0lJYUBAwbQpEkTPv74Y9cIrRfj4IuHbTOtWRfh9ubwzGb429A8oTUl3XRNaIVrd7yav+0Ui2L/asZ6NIo7ygY69NMREftw+K+baWlpBATk3fP58nF6ejqlSpXKfdxkMhEZGcmoUaOIiIhg27ZtjBgxgqCgIDp16uTQukVEXNXixYuJi4tj5cqVrFq1Ks9zu3btMqiqAlitsHchrBwDmSng5Qf3TIC/DQdPr2uGJ6aargmtl13e8eq3c6lM+uYAAGMeqEubiHLF+imISPFxeHANDAwkIyMjz2OXj4OCgvI83qNHD3r06JF73Lp1a3r06MHKlSsVXEVECmngwIEMHDjQ6DJu7FI8LB8Fh7+zHVdqYttMoHzdAj/k4g12tDqRmMqLi/eTY7HyYKPbGNy2xnXHi4hzc3hwjYiIIDk5mcTERMLDbbcgOXbsGBUrViQkJCTP2JiYmGtmV00mE35+fg6tWUREitn+r+G7f0DGBfD0gbtfhFajwOv6P6ZK3WBHq6lrfiMxNYu6FUN4q3cjNWOJuDiHr3GtVq0aTZs25fXXXyc1NZXTp08zc+ZMevfufc3Y1NRUJk+ezMGDB7FYLKxfv57ly5fTt29fR5ctIiLFIS0RFg2AmL/bQmvFhvD0emg7+oahFSA82LfAHa8qlvLj1z8vUcrfmzn9m6oZS8QNGLJz1rRp08jJyeHee+/l4Ycfpk2bNgwdOhSAqKgovvnmGwAGDBjA448/zvDhw4mKimLq1KlMmTKF6OhoI8oWERF7+vVbmHkXHFwKnt7Qbiw8tQ4qNij0KUIDffPd8SqifDB/XszCwwOm9YuiatmgAs4gIq7Ew2q1Wo0uojiZzWZ2795NZGQkXl7XLuwXEZHrs/t1ND3J1ny17yvbcfl6trWslSJv+pSX7+N6KTObU0npPL9oDzkWK2MeqMPQu2vdes0i4hT0dxMREXGcw6vg22chNR48PKH1KGj3InjfWu/C5R2v4i9m8vS8HeRYrHRuWJEh7WraqXARcQYKriIiUvwykmHVONjzhe04vDb0mA23N7XbS5hyLAyZv4Nzl7KoWS6Ioe1qsft0coE7aYmI61FwFRGR4nVkLXwzAi7FAR7Qcji0/yf4+Nv1ZSZ9e4Cdp5IJ9vOmTKAvXT7YnPvc1TtpiYhrMqQ5S0RESoCsS/DNs/B5L1toDasJf18F979q99D65fZTfL7tFB5A1bKBxJ68kOf5q3fSEhHXpOAqIiL2d3wDzGwJOz+1Hbd4xrZl6x132f2ldp26wEvLbDtjDWxVjQNxF/Mdd3knLRFxXQquIiJiX3/sgM+6QcopKF0VnvwOOk0B30C7v9S5S5k8M38HJrOFjvUr8GCj2647/tINdtoSEeemNa4iImJf/qUgvA5Ubwsd/gV+wcXyMqYcC8M+30n8xSxqlQ/m7YcjiU/JvO7HhNxgpy0RcW4lJriaTKZ87z/o6emJt7d3nnEF8fDwwMfH56bGZmdnU9Atc4trLICvr+9Njc3JycFisdhlrI+PT+42i8U11mw2Yzab7TLW29sbT09PpxlrsVjIyckpcKyXl1fu17YzjLVarWRnFzyrVZSxV35/FtdYuP73cmHGXvn9IEB4BAzfXuwv8+p3B/nlxAVC/Lz5sH9Tgv28Mf+1k9bGI4nXjG8bEU54sP6vRFxZiQmu77zzTr4/eCMiInj00Udzj6dOnVrgD7yqVavy5JNP5h6///77pKen5zu2UqVKPPXUU7nHM2bMICUlJd+x5cqVy905DOCjjz4iISEh37GhoaGMHDky9/i///0vcXFx+Y4NDAxk9OjRuceff/45J0+ezHesj48P48ePzz1etGgRR44cyXcswMsvv5z7/pIlSzh48GCBY8eNG5f7g3358uXs2bOnwLEvvPACQUG2HW5Wr15NbGxsgWOfe+45SpcuDcAPP/zA1q1bCxw7ZMgQypcvD8CmTZvYsGFDgWMHDRpE5cqVAfj5559Zu3ZtgWMHDBhAtWrVANixYwcrV64scGy/fv2oXbs2APv27WPZsmUFju3duzf169cH4NdffyUmJqbAsd27dycyMhKAo0ePsmDBggLHdurUiebNmwNw6tQpPv300wLHdujQgVatWgFw9uxZ5s6dW+DYdu3acffddwOQkJDArFmzChz7t7/9jfvvvx+AlJQU3n///QLHRkdH8+CDDwKQnp7O1KlTCxzbuHFjevToAdh+SXvjjTcKHFuvXj369OmTe3y9sYW5Rlz5/SCOsSj2NJ9tPYmHB7z3SCQ1ytlmdS/vpDX26715wmvbiHCm9GqkW2KJuLgSE1xFRMQ97DmdzD+X7gdgVIfa3HtnhTzPVyodwPR+Ubk7aYX4+xAerPu4iriDErPla7169bRUQEsFCjVWSwVubayWCrgfZ9o6O+FSFl2nb+bPi5ncV68Ccx5viqenh6E1iYjjlJgZV19f30JdcIvyA6goY68Mm44am5Ju4nRyKhczs4u8c8yVP6hdYeyVYcjdxnp6ehb6a80Zxnp4eLjUWCi+73uxr2yzhWFf7OTPi5nUKBfEOw83VmgVKWFKTHAtaeKSM3jx671sumqNl3aOEZHilpJuIjHVdFO/NF/Pa9/9yvbfkwj28+bD/tG6Q4BICaTg6oZS0k3XhFb4384x0/tFaa2XiBSL4vql+esdf/DfLScAeLdvJLXKF88ttkTEuWkDAjeUmGq6JrRepp1jRKS43OiX5pvdbnXfHymMW7IPgGfvjeC+ehVu8BEi4q4UXN3QxRvsDKOdY0SkOBTHL83nU7MYPC8WU46Fe+uWZ+S9Ebdapoi4MAVXN1TqBuu+tC5MRIqDvX9pvtyMFZeSSY3wIN59JFLNWCIlnIKrGwr/a+eY/GjnGBEpLjfzS3NKuolj51LZdeoCxxJS8ywneGPFIX4+nkSQrxdz+je94flFxP2pOcsNaecYETFCeBG3W71eI9fPx8/zyU+/A/D2w5FEVAgp3uJFxCWUmA0InOHG2Y52+ZY02jlGRG5FUa6jcckZBf7SfNsVdxVISTcxfMGufNfERlUpzcGzF8nKsTDinlr84/469vtkRMSlacbVjYUGKqiKiGMVdrvV6zVy7TqdDED7OuUY2aF2cZcsIi5EwVVEROyqML8036iR67ZQf957JAovNWOJyBXUnCUiIg53o0arV7s3IDRAzVgikpeCq4iIONz17n5yZ8UQoquVcXBFIuIKFFxFRMThLt/95OrwWqVMAJ882Uzr80UkXwquIiJiiEqlA5jcvT4VS/kD0KxaGZYNa5Xn7gMiIldSc5aIiBgix2zhn8sO8OfFTKqWDWTuE80IDdS6VhEpmGZcRUTEEG+tPsymI4kE+HjxYf9ohVYRuSEFVxERcbhv98QxZ+NxAKb2aUyditoZS0RuTMFVREQc6tezFxkTsxeAZ9rV5MFGtxlckYi4CgVXERFxmOR0E0/PiyUj20ybiHBGd9R2riJSeAquIiLiEGaLlRELdnE6KYMqYbatYbUzlogUhe4qUEKlpJtITDVxMTObUgE+hAfdeItGEZFbcXUzVmldc0SkiBRc3VhB4TQuOYMXv97LpiOJuWPbRoTzZq9GVNL9E0WkGHy39yyzNxwDYErvRtx5WymDKxIRV6Tg6qYKCqdv9GzI2MX78jwOsPFIImO/3sv0flEuN/Oq2WMR53boz4u88NUeAJ5qU51ujSsZXJGIuCoFVzeUkm66JrSCLZyePJ9+zeNXPp+YanKp0KfZYxHnlpKezeB5O8jINtOqVllefKCu0SWJiAtTcL1CUWbu7DnLd/lcqVnZlA70xZRjITUrp8jnvXyerBxzgeE0OSP7uudIy8o2bAazqK97vYDuqrPHIu7EbLHy3MJdnDyfzu1lAvigXxO8vdQTLCI3z5Dgev78eSZOnMj27dvx8vKiW7duvPjii3h7X1vOhg0bmDp1KqdPn+a2225jzJgxtG/f3u41FWXmzp6zfJfPtePkBab1i+Lfqw/z09HzRT7vlTXNfKxJgeP8vAv+oRHo60WpAF+GL9jl8BnMm/k3TUw1udXssYi7eff731h/OAF/H0/m9G9KmSB9P4rIrTHkV9+RI0cSGBjIpk2biImJYevWrfz3v/+9ZtyJEycYMWIEzz33HLGxsYwYMYKRI0cSHx9v13puNHOXkm66qbFFed2/t67Of376PU9oLex5r67peuF01+lk2kSE5/vcxC71mLh0v10+t6K42X/Ti5nXnz2+dIPnRUqipKQk7rvvPrZt21asr7Nq/1k+WHcUgCm9GlG/Umixvp6IlAwOD64nT55k+/btjB49moCAAKpUqcLQoUP5/PPPrxm7ZMkSoqOj6dChA97e3nTu3JlmzZqxcOFCu9ZUmJm7mxlblNeNqlL6mtBa2PNeXdOu08m0qlU237GHz17kjYca0vaq8No2Ipwmd5Rm01H7fG5FcbP/pqX8r7+vecgNnhcpaXbs2EHfvn05depUsb7OmeQM/rHI1ow1qHV1ukdWLtbXE5GSw+FLBY4cOULp0qWpUKFC7mM1a9YkLi6OixcvUqrU/26RcvToUWrXrp3n42vVqsWhQ4fsWlNRZu7sOct35bmyciw3fd6ra/pk8+9M6xcFcM2yg1e6N+C20rYbfyemmriUmU2Ivw/hwb4cT0y76Rpuxc3+m4YH+9I2IpyN+YTethHhhAfrz5Iily1ZsoRp06YxevRoRo0aVayvdTIxjTSTmda1whnbSc1YImI/Dg+uaWlpBATkXbN4+Tg9PT1PcM1vrL+/P+np6XatqSgzd/ac5bvyXNf78/6Nznt1TekmM88u2MXfW1fn762qExrgQ5lAX8KD/9fsFBp4beNTKf/rz6gW1wzmzf6bhgb68mavRoz9em+e8No2IpwpvRppfavIFVq3bk3Xrl3x9vYu9uD6t5plWft8W6qVDVIzlojYlcODa2BgIBkZGXkeu3wcFBSU5/GAgAAyMzPzPJaZmXnNuFtVlJk7e87yXXmuy3/ez2+5wI3Om19N6SYzH/x4lLYR4YXurjdqBvNWXrdSAbPHCq0ieZUrV85hr+Xh4UGt8iEOez0RKTkc/qtwREQEycnJJCb+L6QcO3aMihUrEhKS90JXu3Ztjhw5kuexo0ePEhERYdeaLs/c5bfu8+qZu6KMLcrrfrL5dwa2qn7N2tTCnNdeNdnzcyuKW33d0EBfapYPJvKOMtQsH6zQKiIi4qY8rFar1dEv+uijj1KxYkVeeeUVLly4wJAhQ+jYsSMjRozIM+7YsWM89NBDvPnmm9x///2sWbOGsWPHsmzZMqpXr16o1zKbzezevZvIyEi8vLyuO/byfUQLM3NXlLE3cvlcaVnZhAb4YjJbSMvKKfJ57VWTPT83V3hdkZKmTp06fPbZZ7Ro0aJQ44tyHRURKU6G3Md12rRpvPLKK9x77714enrSo0cPhg4dCkBUVBSTJk2iW7du1KxZkxkzZjB16lQmTJhA5cqVmT59eqFDa1Hlt+7THmMddS5nO4+rvK6IiIi4BkOCa3h4ONOmTcv3uV27duU5btOmDW3atHFEWSIiIiLixLTlq4hICXP48GGjSxARuSm6T4mIiIiIuAQFVxERERFxCQquIiIiIuISFFxFRERExCUouIqIiIiIS1BwFRERERGXoOAqIiIiIi5BwVVEREREXIKCq4iIiIi4BLffOctqtQJgNpsNrkRE3JmnpyceHh5Gl1EsdB0VEUe50bXU7YOrxWIBYN++fQZXIiLuLDIyEi8vL6PLKBa6joqIo9zoWuphvfyrtJuyWCzk5OS49WyIiBjPna8xuo6KiKPc6Drj9sFVRERERNyDmrNERERExCUouIqIiIiIS1BwFRERERGXoOAqIiIiIi5BwVVEREREXIKCq4iIiIi4BAVXEREREXEJCq5O7tChQwwcOJDmzZvTqlUrxowZQ1JSktFlOSWz2Uz//v0ZO3as0aU4neTkZMaMGUOLFi1o1qwZQ4cO5dy5c0aX5VQOHDjAY489RnR0NK1bt+bVV1/FZDIZXZbYga6jhafraMF0Hb0xR1xHFVydWGZmJoMGDSIqKorNmzezfPlykpOTGT9+vNGlOaUPPviA2NhYo8twSiNGjCA9PZ3vv/+edevW4eXlxcSJE40uy2lYLBYGDx5Mx44d2b59OzExMWzevJmPPvrI6NLkFuk6WjS6jhZM19Hrc9R11NuuZxO7iouLo27dugwbNgwvLy98fX3p27cvY8aMMbo0p7N161bWrFnD/fffb3QpTmf//v3s2bOHLVu2EBwcDMDkyZNJSEgwuDLnkZKSQkJCAhaLhcubCXp6ehIQEGBwZXKrdB0tPF1HC6br6I056jqqGVcnVqNGDebOnYuXl1fuY6tXr6Z+/foGVuV8zp8/z4QJE3j77bcVNPKxd+9eatWqxaJFi7jvvvto3bo1U6ZMoVy5ckaX5jTKlCnDk08+yZQpU2jYsCHt2rWjWrVqPPnkk0aXJrdI19HC0XX0+nQdvTFHXUcVXF2E1Wrl3XffZd26dUyYMMHocpyGxWJh9OjRDBw4kLp16xpdjlNKSUnh8OHDnDhxgiVLlrB06VLi4+N58cUXjS7NaVgsFvz9/Zk4cSK7d+9m+fLlHDt2jGnTphldmtiRrqP503X0xnQdvTFHXUcVXF1Aamoqzz77LN9++y3z58+nTp06RpfkNObMmYOvry/9+/c3uhSn5evrC8CECRMIDg4mPDyckSNHsmHDBtLS0gyuzjl8//33rF69mkcffRRfX18iIiIYNmwYCxYsMLo0sRNdRwum6+iN6Tp6Y466jmqNq5M7deoUTz31FJUqVSImJoawsDCjS3Iqy5Yt49y5c0RHRwO2RgyAtWvXqsHgL7Vq1cJisZCdnY2fnx9g+80YyF2HVNKdPXv2ms5Xb29vfHx8DKpI7EnX0evTdfTGdB29MUddRzXj6sRSUlIYMGAATZo04eOPP9bFNh+rVq1i586dxMbGEhsbS5cuXejSpYsutldo2bIlVapUYfz48aSlpZGUlMS7775Lhw4dcpsMSrrWrVuTkJDA7NmzMZvNnD59mlmzZtG1a1ejS5NbpOvojek6emO6jt6Yo66jCq5ObPHixcTFxbFy5UqaNm1KVFRU7ptIYfn4+DBv3jy8vLzo2LEjHTt2pGLFirz++utGl+Y0atWqxZw5c/jxxx9p0aIFTzzxBPfccw+jRo0yujS5RbqOij3oOnpjjrqOelg1xy0iIiIiLkAzriIiIiLiEhRcRURERMQlKLiKiIiIiEtQcBURERERl6DgKiIiIiIuQcFVRERERFyCgquIiIiIuAQFVxERERFxCQquIiIiIuISFFxFRERExCUouIoA3333HQ0aNODQoUMAHDx4kEaNGrFx40aDKxMRcQ26joojeFitVqvRRYg4g3HjxnHgwAHmzZvHww8/TMeOHXn++eeNLktExGXoOirFTcFV5C/p6en07NkTk8lEpUqV+PTTT/Hy8jK6LBERl6HrqBQ3LRUQ+UtgYCC9evXizJkzPPTQQ7rYiogUka6jUtw04yryl1OnTtGjRw86d+7M999/z7Jly6hYsaLRZYmIuAxdR6W4KbiKANnZ2fTr148777yTyZMnM3z4cFJSUvj000/x9NQfJkREbkTXUXEEfSWJAO+//z4XLlxg7NixALzyyiscPXqUOXPmGFyZiIhr0HVUHEEzriIiIiLiEjTjKiIiIiIuQcFVRERERFyCgquIiIiIuAQFVxERERFxCQquIiIiIuISFFxFRERExCUouIqIiIiIS1BwFRERERGXoOAqIiIiIi5BwVVEREREXIKCq4iIiIi4hP8HUsMtznBarhoAAAAASUVORK5CYII=", - "application/papermill.record/text/plain": "<Figure size 800x500 with 2 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "cooks-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import seaborn as sns\n", "import statsmodels.api as sm\n", @@ -3903,7 +2735,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "cef42308", "metadata": { "editable": true, @@ -3912,92 +2744,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.97</td>\n", - " <td>3.04</td>\n", - " <td>41.42</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>119.93</td>\n", - " <td>132.00</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.95</td>\n", - " <td>0.55</td>\n", - " <td>-16.17</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-10.05</td>\n", - " <td>-7.85</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>baby_sleep</td>\n", - " <td>0.01</td>\n", - " <td>0.27</td>\n", - " <td>0.04</td>\n", - " <td>0.97</td>\n", - " <td>0.82</td>\n", - " <td>0.81</td>\n", - " <td>-0.53</td>\n", - " <td>0.55</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 125.97 3.04 41.42 0.00 0.82 0.81 119.93 132.00\n", - "1 dan_sleep -8.95 0.55 -16.17 0.00 0.82 0.81 -10.05 -7.85\n", - "2 baby_sleep 0.01 0.27 0.04 0.97 0.82 0.81 -0.53 0.55" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import pingouin as pg\n", @@ -4029,7 +2776,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "41f92756", "metadata": { "editable": true, @@ -4045,7 +2792,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "d0b77749", "metadata": { "editable": true, @@ -4054,92 +2801,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>126.36</td>\n", - " <td>3.00</td>\n", - " <td>42.19</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.82</td>\n", - " <td>120.41</td>\n", - " <td>132.30</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.83</td>\n", - " <td>0.55</td>\n", - " <td>-16.13</td>\n", - " <td>0.00</td>\n", - " <td>0.82</td>\n", - " <td>0.82</td>\n", - " <td>-9.91</td>\n", - " <td>-7.74</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>baby_sleep</td>\n", - " <td>-0.13</td>\n", - " <td>0.28</td>\n", - " <td>-0.48</td>\n", - " <td>0.63</td>\n", - " <td>0.82</td>\n", - " <td>0.82</td>\n", - " <td>-0.68</td>\n", - " <td>0.41</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 126.36 3.00 42.19 0.00 0.82 0.82 120.41 132.30\n", - "1 dan_sleep -8.83 0.55 -16.13 0.00 0.82 0.82 -9.91 -7.74\n", - "2 baby_sleep -0.13 0.28 -0.48 0.63 0.82 0.82 -0.68 0.41" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "predictors = df_2[['dan_sleep', 'baby_sleep']]\n", "outcome = df_2['dan_grump']\n", @@ -4183,7 +2845,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "09e7adf8", "metadata": { "editable": true, @@ -4194,21 +2856,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "res-hist-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import pingouin as pg\n", @@ -4269,7 +2917,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "b566420f", "metadata": { "editable": true, @@ -4278,54 +2926,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>W</th>\n", - " <th>pval</th>\n", - " <th>normal</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>Residuals</th>\n", - " <td>0.99</td>\n", - " <td>0.84</td>\n", - " <td>True</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " W pval normal\n", - "Residuals 0.99 0.84 True" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sw = pg.normality(res, method = 'shapiro')\n", "sw.round(2)" @@ -4348,7 +2949,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "a75c505c", "metadata": { "editable": true, @@ -4357,18 +2958,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import statsmodels.api as sm\n", "\n", @@ -4417,7 +3007,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "20d8719d", "metadata": { "editable": true, @@ -4428,18 +3018,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 1500x500 with 2 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Define a figure with two panels\n", "import matplotlib.pyplot as plt\n", @@ -4483,7 +3062,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "55be1b48", "metadata": { "editable": true, @@ -4494,21 +3073,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "<Figure size 640x480 with 1 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "fitted_v_observed-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "predictors = df[['dan_sleep', 'baby_sleep']]\n", "outcome = df['dan_grump']\n", @@ -4570,7 +3135,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "e2586aee", "metadata": { "editable": true, @@ -4581,21 +3146,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "<Figure size 800x500 with 3 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "resid1-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Define a figure with two panels\n", "import matplotlib.pyplot as plt\n", @@ -4675,7 +3226,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "10bf9e26", "metadata": { "editable": true, @@ -4684,105 +3235,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>1.540</td>\n", - " <td>57.642</td>\n", - " <td>0.027</td>\n", - " <td>0.979</td>\n", - " <td>0.825</td>\n", - " <td>0.819</td>\n", - " <td>-112.878</td>\n", - " <td>115.958</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>2.867</td>\n", - " <td>5.494</td>\n", - " <td>0.522</td>\n", - " <td>0.603</td>\n", - " <td>0.825</td>\n", - " <td>0.819</td>\n", - " <td>-8.039</td>\n", - " <td>13.773</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>baby_sleep</td>\n", - " <td>0.011</td>\n", - " <td>0.266</td>\n", - " <td>0.043</td>\n", - " <td>0.966</td>\n", - " <td>0.825</td>\n", - " <td>0.819</td>\n", - " <td>-0.517</td>\n", - " <td>0.540</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>Tukey</td>\n", - " <td>0.010</td>\n", - " <td>0.005</td>\n", - " <td>2.162</td>\n", - " <td>0.033</td>\n", - " <td>0.825</td>\n", - " <td>0.819</td>\n", - " <td>0.001</td>\n", - " <td>0.020</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] CI[97.5%]\n", - "0 Intercept 1.540 57.642 0.027 0.979 0.825 0.819 -112.878 115.958\n", - "1 dan_sleep 2.867 5.494 0.522 0.603 0.825 0.819 -8.039 13.773\n", - "2 baby_sleep 0.011 0.266 0.043 0.966 0.825 0.819 -0.517 0.540\n", - "3 Tukey 0.010 0.005 2.162 0.033 0.825 0.819 0.001 0.020" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# square the fitted values for our model, and add them to our dataframe\n", "df['Tukey'] = mod2['pred']**2\n", @@ -4830,7 +3283,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "7e60bcee", "metadata": { "editable": true, @@ -4841,28 +3294,7 @@ "hide-input" ] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal value for lambda: 1.7912772737277842\n" - ] - }, - { - "data": { - "application/papermill.record/image/png": 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", - "application/papermill.record/text/plain": "<Figure size 800x500 with 2 Axes>" - }, - "metadata": { - "scrapbook": { - "mime_prefix": "application/papermill.record/", - "name": "boxcox-fig" - } - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from scipy.stats import boxcox\n", "\n", @@ -4940,7 +3372,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "554b5a3c", "metadata": { "editable": true, @@ -4951,18 +3383,7 @@ "hide-input" ] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "<Figure size 640x480 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "\n", "predictors = df[['dan_sleep', 'baby_sleep']]\n", @@ -4970,14 +3391,11 @@ "\n", "mod2 = pg.linear_regression(predictors, outcome, as_dataframe = False)\n", "\n", - "#df_slplot = pd.DataFrame(\n", - "# {'fitted': mod2['pred'],\n", - "# 'sqrt_abs_stand_res': np.sqrt(np.abs(mod2['residuals']))\n", - "# })\n", "\n", + "# Thanks to Ivan Savov (https://github.com/ivanistheone) for contributing this solution for getting the standardized residuals\n", "SS_resid = np.sum(mod2[\"residuals\"]**2)\n", "n = len(mod2['residuals'])\n", - "p = 2 # bcs 2 predictors = 'dan_sleep' and 'baby_sleep' \n", + "p = len(list(predictors))\n", "sigmahat = np.sqrt(abs( SS_resid/(n-p-1) ))\n", "stand_res = mod2['residuals'] / sigmahat\n", "df_slplot = pd.DataFrame(\n", @@ -5037,19 +3455,10 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "1b4b0ee0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lagrange multiplier statistic: 0.4886831614554388\n", - "p: 0.7832200556890354\n" - ] - } - ], + "outputs": [], "source": [ "import statsmodels.formula.api as smf\n", "import statsmodels.stats.api as sms\n", @@ -5080,100 +3489,10 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "7f78b86a", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>names</th>\n", - " <th>coef</th>\n", - " <th>se</th>\n", - " <th>T</th>\n", - " <th>pval</th>\n", - " <th>r2</th>\n", - " <th>adj_r2</th>\n", - " <th>CI[2.5%]</th>\n", - " <th>CI[97.5%]</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>Intercept</td>\n", - " <td>125.9656</td>\n", - " <td>3.0409</td>\n", - " <td>41.4231</td>\n", - " <td>0.0000</td>\n", - " <td>0.8161</td>\n", - " <td>0.8123</td>\n", - " <td>119.9301</td>\n", - " <td>132.0010</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>dan_sleep</td>\n", - " <td>-8.9502</td>\n", - " <td>0.5535</td>\n", - " <td>-16.1715</td>\n", - " <td>0.0000</td>\n", - " <td>0.8161</td>\n", - " <td>0.8123</td>\n", - " <td>-10.0487</td>\n", - " <td>-7.8518</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>baby_sleep</td>\n", - " <td>0.0105</td>\n", - " <td>0.2711</td>\n", - " <td>0.0388</td>\n", - " <td>0.9691</td>\n", - " <td>0.8161</td>\n", - " <td>0.8123</td>\n", - " <td>-0.5275</td>\n", - " <td>0.5485</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " names coef se T pval r2 adj_r2 CI[2.5%] \\\n", - "0 Intercept 125.9656 3.0409 41.4231 0.0000 0.8161 0.8123 119.9301 \n", - "1 dan_sleep -8.9502 0.5535 -16.1715 0.0000 0.8161 0.8123 -10.0487 \n", - "2 baby_sleep 0.0105 0.2711 0.0388 0.9691 0.8161 0.8123 -0.5275 \n", - "\n", - " CI[97.5%] \n", - "0 132.0010 \n", - "1 -7.8518 \n", - "2 0.5485 " - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "predictors = df[['dan_sleep', 'baby_sleep']]\n", "outcome = df['dan_grump']\n", @@ -5193,145 +3512,10 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "6cf22983", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<table class=\"simpletable\">\n", - "<caption>OLS Regression Results</caption>\n", - "<tr>\n", - " <th>Dep. Variable:</th> <td>dan_grump</td> <th> R-squared: </th> <td> 0.816</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.812</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 215.2</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Date:</th> <td>Fri, 26 Apr 2024</td> <th> Prob (F-statistic):</th> <td>2.15e-36</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Time:</th> <td>15:51:19</td> <th> Log-Likelihood: </th> <td> -287.48</td>\n", - "</tr>\n", - "<tr>\n", - " <th>No. Observations:</th> <td> 100</td> <th> AIC: </th> <td> 581.0</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Df Residuals:</th> <td> 97</td> <th> BIC: </th> <td> 588.8</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n", - "</tr>\n", - "<tr>\n", - " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n", - "</tr>\n", - "</table>\n", - "<table class=\"simpletable\">\n", - "<tr>\n", - " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n", - "</tr>\n", - "<tr>\n", - " <th>Intercept</th> <td> 125.9656</td> <td> 3.041</td> <td> 41.423</td> <td> 0.000</td> <td> 119.930</td> <td> 132.001</td>\n", - "</tr>\n", - "<tr>\n", - " <th>dan_sleep</th> <td> -8.9502</td> <td> 0.553</td> <td> -16.172</td> <td> 0.000</td> <td> -10.049</td> <td> -7.852</td>\n", - "</tr>\n", - "<tr>\n", - " <th>baby_sleep</th> <td> 0.0105</td> <td> 0.271</td> <td> 0.039</td> <td> 0.969</td> <td> -0.527</td> <td> 0.549</td>\n", - "</tr>\n", - "</table>\n", - "<table class=\"simpletable\">\n", - "<tr>\n", - " <th>Omnibus:</th> <td> 0.593</td> <th> Durbin-Watson: </th> <td> 2.120</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Prob(Omnibus):</th> <td> 0.743</td> <th> Jarque-Bera (JB): </th> <td> 0.218</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Skew:</th> <td>-0.053</td> <th> Prob(JB): </th> <td> 0.897</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Kurtosis:</th> <td> 3.203</td> <th> Cond. No. </th> <td> 76.9</td>\n", - "</tr>\n", - "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/latex": [ - "\\begin{center}\n", - "\\begin{tabular}{lclc}\n", - "\\toprule\n", - "\\textbf{Dep. Variable:} & dan\\_grump & \\textbf{ R-squared: } & 0.816 \\\\\n", - "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.812 \\\\\n", - "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 215.2 \\\\\n", - "\\textbf{Date:} & Fri, 26 Apr 2024 & \\textbf{ Prob (F-statistic):} & 2.15e-36 \\\\\n", - "\\textbf{Time:} & 15:51:19 & \\textbf{ Log-Likelihood: } & -287.48 \\\\\n", - "\\textbf{No. Observations:} & 100 & \\textbf{ AIC: } & 581.0 \\\\\n", - "\\textbf{Df Residuals:} & 97 & \\textbf{ BIC: } & 588.8 \\\\\n", - "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n", - "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lcccccc}\n", - " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", - "\\midrule\n", - "\\textbf{Intercept} & 125.9656 & 3.041 & 41.423 & 0.000 & 119.930 & 132.001 \\\\\n", - "\\textbf{dan\\_sleep} & -8.9502 & 0.553 & -16.172 & 0.000 & -10.049 & -7.852 \\\\\n", - "\\textbf{baby\\_sleep} & 0.0105 & 0.271 & 0.039 & 0.969 & -0.527 & 0.549 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 0.593 & \\textbf{ Durbin-Watson: } & 2.120 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.743 & \\textbf{ Jarque-Bera (JB): } & 0.218 \\\\\n", - "\\textbf{Skew:} & -0.053 & \\textbf{ Prob(JB): } & 0.897 \\\\\n", - "\\textbf{Kurtosis:} & 3.203 & \\textbf{ Cond. No. } & 76.9 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "%\\caption{OLS Regression Results}\n", - "\\end{center}\n", - "\n", - "Notes: \\newline\n", - " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "<class 'statsmodels.iolib.summary.Summary'>\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: dan_grump R-squared: 0.816\n", - "Model: OLS Adj. R-squared: 0.812\n", - "Method: Least Squares F-statistic: 215.2\n", - "Date: Fri, 26 Apr 2024 Prob (F-statistic): 2.15e-36\n", - "Time: 15:51:19 Log-Likelihood: -287.48\n", - "No. Observations: 100 AIC: 581.0\n", - "Df Residuals: 97 BIC: 588.8\n", - "Df Model: 2 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "Intercept 125.9656 3.041 41.423 0.000 119.930 132.001\n", - "dan_sleep -8.9502 0.553 -16.172 0.000 -10.049 -7.852\n", - "baby_sleep 0.0105 0.271 0.039 0.969 -0.527 0.549\n", - "==============================================================================\n", - "Omnibus: 0.593 Durbin-Watson: 2.120\n", - "Prob(Omnibus): 0.743 Jarque-Bera (JB): 0.218\n", - "Skew: -0.053 Prob(JB): 0.897\n", - "Kurtosis: 3.203 Cond. No. 76.9\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "smf.ols('dan_grump ~ dan_sleep+baby_sleep', data=df).fit().summary()" ] @@ -5351,145 +3535,10 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "3588a5e1", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<table class=\"simpletable\">\n", - "<caption>OLS Regression Results</caption>\n", - "<tr>\n", - " <th>Dep. Variable:</th> <td>dan_grump</td> <th> R-squared: </th> <td> 0.816</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.812</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 201.7</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Date:</th> <td>Fri, 26 Apr 2024</td> <th> Prob (F-statistic):</th> <td>2.78e-35</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Time:</th> <td>15:51:19</td> <th> Log-Likelihood: </th> <td> -287.48</td>\n", - "</tr>\n", - "<tr>\n", - " <th>No. Observations:</th> <td> 100</td> <th> AIC: </th> <td> 581.0</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Df Residuals:</th> <td> 97</td> <th> BIC: </th> <td> 588.8</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Df Model:</th> <td> 2</td> <th> </th> <td> </td> \n", - "</tr>\n", - "<tr>\n", - " <th>Covariance Type:</th> <td>HC1</td> <th> </th> <td> </td> \n", - "</tr>\n", - "</table>\n", - "<table class=\"simpletable\">\n", - "<tr>\n", - " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", - "</tr>\n", - "<tr>\n", - " <th>Intercept</th> <td> 125.9656</td> <td> 3.149</td> <td> 39.996</td> <td> 0.000</td> <td> 119.793</td> <td> 132.138</td>\n", - "</tr>\n", - "<tr>\n", - " <th>dan_sleep</th> <td> -8.9502</td> <td> 0.597</td> <td> -14.987</td> <td> 0.000</td> <td> -10.121</td> <td> -7.780</td>\n", - "</tr>\n", - "<tr>\n", - " <th>baby_sleep</th> <td> 0.0105</td> <td> 0.282</td> <td> 0.037</td> <td> 0.970</td> <td> -0.541</td> <td> 0.563</td>\n", - "</tr>\n", - "</table>\n", - "<table class=\"simpletable\">\n", - "<tr>\n", - " <th>Omnibus:</th> <td> 0.593</td> <th> Durbin-Watson: </th> <td> 2.120</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Prob(Omnibus):</th> <td> 0.743</td> <th> Jarque-Bera (JB): </th> <td> 0.218</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Skew:</th> <td>-0.053</td> <th> Prob(JB): </th> <td> 0.897</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Kurtosis:</th> <td> 3.203</td> <th> Cond. No. </th> <td> 76.9</td>\n", - "</tr>\n", - "</table><br/><br/>Notes:<br/>[1] Standard Errors are heteroscedasticity robust (HC1)" - ], - "text/latex": [ - "\\begin{center}\n", - "\\begin{tabular}{lclc}\n", - "\\toprule\n", - "\\textbf{Dep. Variable:} & dan\\_grump & \\textbf{ R-squared: } & 0.816 \\\\\n", - "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.812 \\\\\n", - "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 201.7 \\\\\n", - "\\textbf{Date:} & Fri, 26 Apr 2024 & \\textbf{ Prob (F-statistic):} & 2.78e-35 \\\\\n", - "\\textbf{Time:} & 15:51:19 & \\textbf{ Log-Likelihood: } & -287.48 \\\\\n", - "\\textbf{No. Observations:} & 100 & \\textbf{ AIC: } & 581.0 \\\\\n", - "\\textbf{Df Residuals:} & 97 & \\textbf{ BIC: } & 588.8 \\\\\n", - "\\textbf{Df Model:} & 2 & \\textbf{ } & \\\\\n", - "\\textbf{Covariance Type:} & HC1 & \\textbf{ } & \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lcccccc}\n", - " & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", - "\\midrule\n", - "\\textbf{Intercept} & 125.9656 & 3.149 & 39.996 & 0.000 & 119.793 & 132.138 \\\\\n", - "\\textbf{dan\\_sleep} & -8.9502 & 0.597 & -14.987 & 0.000 & -10.121 & -7.780 \\\\\n", - "\\textbf{baby\\_sleep} & 0.0105 & 0.282 & 0.037 & 0.970 & -0.541 & 0.563 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 0.593 & \\textbf{ Durbin-Watson: } & 2.120 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.743 & \\textbf{ Jarque-Bera (JB): } & 0.218 \\\\\n", - "\\textbf{Skew:} & -0.053 & \\textbf{ Prob(JB): } & 0.897 \\\\\n", - "\\textbf{Kurtosis:} & 3.203 & \\textbf{ Cond. No. } & 76.9 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "%\\caption{OLS Regression Results}\n", - "\\end{center}\n", - "\n", - "Notes: \\newline\n", - " [1] Standard Errors are heteroscedasticity robust (HC1)" - ], - "text/plain": [ - "<class 'statsmodels.iolib.summary.Summary'>\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: dan_grump R-squared: 0.816\n", - "Model: OLS Adj. R-squared: 0.812\n", - "Method: Least Squares F-statistic: 201.7\n", - "Date: Fri, 26 Apr 2024 Prob (F-statistic): 2.78e-35\n", - "Time: 15:51:19 Log-Likelihood: -287.48\n", - "No. Observations: 100 AIC: 581.0\n", - "Df Residuals: 97 BIC: 588.8\n", - "Df Model: 2 \n", - "Covariance Type: HC1 \n", - "==============================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "Intercept 125.9656 3.149 39.996 0.000 119.793 132.138\n", - "dan_sleep -8.9502 0.597 -14.987 0.000 -10.121 -7.780\n", - "baby_sleep 0.0105 0.282 0.037 0.970 -0.541 0.563\n", - "==============================================================================\n", - "Omnibus: 0.593 Durbin-Watson: 2.120\n", - "Prob(Omnibus): 0.743 Jarque-Bera (JB): 0.218\n", - "Skew: -0.053 Prob(JB): 0.897\n", - "Kurtosis: 3.203 Cond. No. 76.9\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors are heteroscedasticity robust (HC1)\n", - "\"\"\"" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "smf.ols('dan_grump ~ dan_sleep+baby_sleep', data=df).fit(cov_type='HC1').summary()" ] @@ -5532,85 +3581,10 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "d3ccbeda", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>Intercept</th>\n", - " <th>dan_sleep</th>\n", - " <th>baby_sleep</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>1</td>\n", - " <td>7.59</td>\n", - " <td>10.18</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>1</td>\n", - " <td>7.91</td>\n", - " <td>11.66</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>1</td>\n", - " <td>5.14</td>\n", - " <td>7.92</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>1</td>\n", - " <td>7.71</td>\n", - " <td>9.61</td>\n", - " </tr>\n", - " <tr>\n", - " <th>4</th>\n", - " <td>1</td>\n", - " <td>6.68</td>\n", - " <td>9.75</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " Intercept dan_sleep baby_sleep\n", - "0 1 7.59 10.18\n", - "1 1 7.91 11.66\n", - "2 1 5.14 7.92\n", - "3 1 7.71 9.61\n", - "4 1 6.68 9.75" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "matrix = pd.DataFrame({'Intercept': [1]*len(df['dan_sleep']),\n", " 'dan_sleep': df['dan_sleep'], \n", @@ -5629,67 +3603,10 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "c433a0f4", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>VIF</th>\n", - " <th>variable</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>48.784569</td>\n", - " <td>Intercept</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>1.651038</td>\n", - " <td>dan_sleep</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>1.651038</td>\n", - " <td>baby_sleep</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " VIF variable\n", - "0 48.784569 Intercept\n", - "1 1.651038 dan_sleep\n", - "2 1.651038 baby_sleep" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "\n", @@ -5712,121 +3629,10 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "0d9d96d6", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>dan_sleep</th>\n", - " <th>baby_sleep</th>\n", - " <th>dan_grump</th>\n", - " <th>day</th>\n", - " <th>Tukey</th>\n", - " <th>dan_sleep_transformed</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>dan_sleep</th>\n", - " <td>1.000000</td>\n", - " <td>0.627949</td>\n", - " <td>-0.903384</td>\n", - " <td>-0.098408</td>\n", - " <td>-0.997024</td>\n", - " <td>0.997866</td>\n", - " </tr>\n", - " <tr>\n", - " <th>baby_sleep</th>\n", - " <td>0.627949</td>\n", - " <td>1.000000</td>\n", - " <td>-0.565964</td>\n", - " <td>-0.010434</td>\n", - " <td>-0.626177</td>\n", - " <td>0.625397</td>\n", - " </tr>\n", - " <tr>\n", - " <th>dan_grump</th>\n", - " <td>-0.903384</td>\n", - " <td>-0.565964</td>\n", - " <td>1.000000</td>\n", - " <td>0.076479</td>\n", - " <td>0.907817</td>\n", - " <td>-0.895394</td>\n", - " </tr>\n", - " <tr>\n", - " <th>day</th>\n", - " <td>-0.098408</td>\n", - " <td>-0.010434</td>\n", - " <td>0.076479</td>\n", - " <td>1.000000</td>\n", - " <td>0.103952</td>\n", - " <td>-0.093393</td>\n", - " </tr>\n", - " <tr>\n", - " <th>Tukey</th>\n", - " <td>-0.997024</td>\n", - " <td>-0.626177</td>\n", - " <td>0.907817</td>\n", - " <td>0.103952</td>\n", - " <td>1.000000</td>\n", - " <td>-0.989864</td>\n", - " </tr>\n", - " <tr>\n", - " <th>dan_sleep_transformed</th>\n", - " <td>0.997866</td>\n", - " <td>0.625397</td>\n", - " <td>-0.895394</td>\n", - " <td>-0.093393</td>\n", - " <td>-0.989864</td>\n", - " <td>1.000000</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " dan_sleep baby_sleep dan_grump day Tukey \\\n", - "dan_sleep 1.000000 0.627949 -0.903384 -0.098408 -0.997024 \n", - "baby_sleep 0.627949 1.000000 -0.565964 -0.010434 -0.626177 \n", - "dan_grump -0.903384 -0.565964 1.000000 0.076479 0.907817 \n", - "day -0.098408 -0.010434 0.076479 1.000000 0.103952 \n", - "Tukey -0.997024 -0.626177 0.907817 0.103952 1.000000 \n", - "dan_sleep_transformed 0.997866 0.625397 -0.895394 -0.093393 -0.989864 \n", - "\n", - " dan_sleep_transformed \n", - "dan_sleep 0.997866 \n", - "baby_sleep 0.625397 \n", - "dan_grump -0.895394 \n", - "day -0.093393 \n", - "Tukey -0.989864 \n", - "dan_sleep_transformed 1.000000 " - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.corr()" ] @@ -5842,97 +3648,10 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "e0029a83", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>Intercept</th>\n", - " <th>dan_sleep</th>\n", - " <th>baby_sleep</th>\n", - " <th>dan_grump</th>\n", - " <th>day</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>1</td>\n", - " <td>7.59</td>\n", - " <td>10.18</td>\n", - " <td>56</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>1</td>\n", - " <td>7.91</td>\n", - " <td>11.66</td>\n", - " <td>60</td>\n", - " <td>2</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>1</td>\n", - " <td>5.14</td>\n", - " <td>7.92</td>\n", - " <td>82</td>\n", - " <td>3</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>1</td>\n", - " <td>7.71</td>\n", - " <td>9.61</td>\n", - " <td>55</td>\n", - " <td>4</td>\n", - " </tr>\n", - " <tr>\n", - " <th>4</th>\n", - " <td>1</td>\n", - " <td>6.68</td>\n", - " <td>9.75</td>\n", - " <td>67</td>\n", - " <td>5</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " Intercept dan_sleep baby_sleep dan_grump day\n", - "0 1 7.59 10.18 56 1\n", - "1 1 7.91 11.66 60 2\n", - "2 1 5.14 7.92 82 3\n", - "3 1 7.71 9.61 55 4\n", - "4 1 6.68 9.75 67 5" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "matrix['dan_grump'] = df['dan_grump']\n", "matrix['day'] = df['day']\n", @@ -5952,79 +3671,10 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "id": "af44556e", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>VIF</th>\n", - " <th>variable</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>922.960321</td>\n", - " <td>Intercept</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>6.148528</td>\n", - " <td>dan_sleep</td>\n", - " </tr>\n", - " <tr>\n", - " <th>2</th>\n", - " <td>1.658389</td>\n", - " <td>baby_sleep</td>\n", - " </tr>\n", - " <tr>\n", - " <th>3</th>\n", - " <td>5.442617</td>\n", - " <td>dan_grump</td>\n", - " </tr>\n", - " <tr>\n", - " <th>4</th>\n", - " <td>1.015119</td>\n", - " <td>day</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " VIF variable\n", - "0 922.960321 Intercept\n", - "1 6.148528 dan_sleep\n", - "2 1.658389 baby_sleep\n", - "3 5.442617 dan_grump\n", - "4 1.015119 day" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "vif = pd.DataFrame()\n", "vif['VIF'] = [variance_inflation_factor(matrix.values, i) for i in range(matrix.shape[1])]\n", @@ -6084,7 +3734,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "id": "f5ac445f", "metadata": {}, "outputs": [], @@ -6103,150 +3753,10 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "id": "6ad334d1", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<table class=\"simpletable\">\n", - "<caption>OLS Regression Results</caption>\n", - "<tr>\n", - " <th>Dep. Variable:</th> <td>dan_grump</td> <th> R-squared: </th> <td> 0.816</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.811</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 142.2</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Date:</th> <td>Fri, 26 Apr 2024</td> <th> Prob (F-statistic):</th> <td>3.42e-35</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Time:</th> <td>15:51:19</td> <th> Log-Likelihood: </th> <td> -287.43</td>\n", - "</tr>\n", - "<tr>\n", - " <th>No. Observations:</th> <td> 100</td> <th> AIC: </th> <td> 582.9</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Df Residuals:</th> <td> 96</td> <th> BIC: </th> <td> 593.3</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Df Model:</th> <td> 3</td> <th> </th> <td> </td> \n", - "</tr>\n", - "<tr>\n", - " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n", - "</tr>\n", - "</table>\n", - "<table class=\"simpletable\">\n", - "<tr>\n", - " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n", - "</tr>\n", - "<tr>\n", - " <th>Intercept</th> <td> 126.2787</td> <td> 3.242</td> <td> 38.945</td> <td> 0.000</td> <td> 119.842</td> <td> 132.715</td>\n", - "</tr>\n", - "<tr>\n", - " <th>dan_sleep</th> <td> -8.9693</td> <td> 0.560</td> <td> -16.016</td> <td> 0.000</td> <td> -10.081</td> <td> -7.858</td>\n", - "</tr>\n", - "<tr>\n", - " <th>baby_sleep</th> <td> 0.0157</td> <td> 0.273</td> <td> 0.058</td> <td> 0.954</td> <td> -0.526</td> <td> 0.558</td>\n", - "</tr>\n", - "<tr>\n", - " <th>day</th> <td> -0.0044</td> <td> 0.015</td> <td> -0.288</td> <td> 0.774</td> <td> -0.035</td> <td> 0.026</td>\n", - "</tr>\n", - "</table>\n", - "<table class=\"simpletable\">\n", - "<tr>\n", - " <th>Omnibus:</th> <td> 0.599</td> <th> Durbin-Watson: </th> <td> 2.120</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Prob(Omnibus):</th> <td> 0.741</td> <th> Jarque-Bera (JB): </th> <td> 0.215</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Skew:</th> <td>-0.037</td> <th> Prob(JB): </th> <td> 0.898</td>\n", - "</tr>\n", - "<tr>\n", - " <th>Kurtosis:</th> <td> 3.215</td> <th> Cond. No. </th> <td> 441.</td>\n", - "</tr>\n", - "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/latex": [ - "\\begin{center}\n", - "\\begin{tabular}{lclc}\n", - "\\toprule\n", - "\\textbf{Dep. Variable:} & dan\\_grump & \\textbf{ R-squared: } & 0.816 \\\\\n", - "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.811 \\\\\n", - "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 142.2 \\\\\n", - "\\textbf{Date:} & Fri, 26 Apr 2024 & \\textbf{ Prob (F-statistic):} & 3.42e-35 \\\\\n", - "\\textbf{Time:} & 15:51:19 & \\textbf{ Log-Likelihood: } & -287.43 \\\\\n", - "\\textbf{No. Observations:} & 100 & \\textbf{ AIC: } & 582.9 \\\\\n", - "\\textbf{Df Residuals:} & 96 & \\textbf{ BIC: } & 593.3 \\\\\n", - "\\textbf{Df Model:} & 3 & \\textbf{ } & \\\\\n", - "\\textbf{Covariance Type:} & nonrobust & \\textbf{ } & \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lcccccc}\n", - " & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]} \\\\\n", - "\\midrule\n", - "\\textbf{Intercept} & 126.2787 & 3.242 & 38.945 & 0.000 & 119.842 & 132.715 \\\\\n", - "\\textbf{dan\\_sleep} & -8.9693 & 0.560 & -16.016 & 0.000 & -10.081 & -7.858 \\\\\n", - "\\textbf{baby\\_sleep} & 0.0157 & 0.273 & 0.058 & 0.954 & -0.526 & 0.558 \\\\\n", - "\\textbf{day} & -0.0044 & 0.015 & -0.288 & 0.774 & -0.035 & 0.026 \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "\\begin{tabular}{lclc}\n", - "\\textbf{Omnibus:} & 0.599 & \\textbf{ Durbin-Watson: } & 2.120 \\\\\n", - "\\textbf{Prob(Omnibus):} & 0.741 & \\textbf{ Jarque-Bera (JB): } & 0.215 \\\\\n", - "\\textbf{Skew:} & -0.037 & \\textbf{ Prob(JB): } & 0.898 \\\\\n", - "\\textbf{Kurtosis:} & 3.215 & \\textbf{ Cond. No. } & 441. \\\\\n", - "\\bottomrule\n", - "\\end{tabular}\n", - "%\\caption{OLS Regression Results}\n", - "\\end{center}\n", - "\n", - "Notes: \\newline\n", - " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified." - ], - "text/plain": [ - "<class 'statsmodels.iolib.summary.Summary'>\n", - "\"\"\"\n", - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: dan_grump R-squared: 0.816\n", - "Model: OLS Adj. R-squared: 0.811\n", - "Method: Least Squares F-statistic: 142.2\n", - "Date: Fri, 26 Apr 2024 Prob (F-statistic): 3.42e-35\n", - "Time: 15:51:19 Log-Likelihood: -287.43\n", - "No. Observations: 100 AIC: 582.9\n", - "Df Residuals: 96 BIC: 593.3\n", - "Df Model: 3 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "Intercept 126.2787 3.242 38.945 0.000 119.842 132.715\n", - "dan_sleep -8.9693 0.560 -16.016 0.000 -10.081 -7.858\n", - "baby_sleep 0.0157 0.273 0.058 0.954 -0.526 0.558\n", - "day -0.0044 0.015 -0.288 0.774 -0.035 0.026\n", - "==============================================================================\n", - "Omnibus: 0.599 Durbin-Watson: 2.120\n", - "Prob(Omnibus): 0.741 Jarque-Bera (JB): 0.215\n", - "Skew: -0.037 Prob(JB): 0.898\n", - "Kurtosis: 3.215 Cond. No. 441.\n", - "==============================================================================\n", - "\n", - "Notes:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", - "\"\"\"" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mod_full.fit().summary()" ] @@ -6262,7 +3772,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "66ce59c1", "metadata": {}, "outputs": [], @@ -6287,21 +3797,10 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "1ed5f89e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'full model AIC: 582.9'" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "get_aic('full model', mod_full)" ] @@ -6317,7 +3816,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "1541abd2", "metadata": { "editable": true, @@ -6326,21 +3825,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Full Model AIC: 582.9',\n", - " 'No Day AIC: 581.0',\n", - " 'No Baby AIC: 580.9',\n", - " 'No Dan AIC: 710.9']" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mod_full = smf.ols('dan_grump ~ dan_sleep + baby_sleep + day', data=df)\n", "mod_no_day = smf.ols('dan_grump ~ dan_sleep + baby_sleep', data=df)\n", @@ -6368,7 +3853,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "b06eb416", "metadata": { "editable": true, @@ -6377,18 +3862,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Full Model AIC: 580.9', 'No Day AIC: 579.0', 'No Dan AIC: 747.7']" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "mod_full = smf.ols('dan_grump ~ dan_sleep + day', data=df)\n", "mod_no_day = smf.ols('dan_grump ~ dan_sleep', data=df)\n", @@ -6452,21 +3926,10 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "6ebd37cd", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['M0 AIC: 580.9', 'M1 AIC: 582.9']" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "M0 = smf.ols('dan_grump ~ dan_sleep + day', data=df)\n", "M1 = smf.ols('dan_grump ~ dan_sleep + day + baby_sleep', data=df)\n", @@ -6496,7 +3959,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "e4b439ff", "metadata": {}, "outputs": [], @@ -6510,21 +3973,10 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "8d456f8d", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['M0 AIC: 580.9 BIC: 588.7', 'M1 AIC: 582.9 BIC: 593.3']" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "models = [M0, M1]\n", "names = ['M0', 'M1']\n", @@ -6567,73 +4019,10 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "b52f0730", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "<div>\n", - "<style scoped>\n", - " .dataframe tbody tr th:only-of-type {\n", - " vertical-align: middle;\n", - " }\n", - "\n", - " .dataframe tbody tr th {\n", - " vertical-align: top;\n", - " }\n", - "\n", - " .dataframe thead th {\n", - " text-align: right;\n", - " }\n", - "</style>\n", - "<table border=\"1\" class=\"dataframe\">\n", - " <thead>\n", - " <tr style=\"text-align: right;\">\n", - " <th></th>\n", - " <th>df_resid</th>\n", - " <th>ssr</th>\n", - " <th>df_diff</th>\n", - " <th>ss_diff</th>\n", - " <th>F</th>\n", - " <th>Pr(>F)</th>\n", - " </tr>\n", - " </thead>\n", - " <tbody>\n", - " <tr>\n", - " <th>0</th>\n", - " <td>97.0</td>\n", - " <td>1837.155916</td>\n", - " <td>0.0</td>\n", - " <td>NaN</td>\n", - " <td>NaN</td>\n", - " <td>NaN</td>\n", - " </tr>\n", - " <tr>\n", - " <th>1</th>\n", - " <td>96.0</td>\n", - " <td>1837.092228</td>\n", - " <td>1.0</td>\n", - " <td>0.063688</td>\n", - " <td>0.003328</td>\n", - " <td>0.954116</td>\n", - " </tr>\n", - " </tbody>\n", - "</table>\n", - "</div>" - ], - "text/plain": [ - " df_resid ssr df_diff ss_diff F Pr(>F)\n", - "0 97.0 1837.155916 0.0 NaN NaN NaN\n", - "1 96.0 1837.092228 1.0 0.063688 0.003328 0.954116" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from statsmodels.stats.anova import anova_lm\n", "\n", diff --git a/searchindex.js b/searchindex.js index 7bad3c0d..9c905917 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["01.01-intro", "01.02-studydesign", "02.01-getting_started_with_python", "02.02-more_python_concepts", 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learn statistics?", "<span class=\"section-number\">2. </span>A brief introduction to research design", "<span class=\"section-number\">3. </span>Getting Started with Python", "<span class=\"section-number\">4. </span>More Python Concepts", "<span class=\"section-number\">5. </span>Descriptive statistics", "<span class=\"section-number\">6. </span>Drawing Graphs", "<span class=\"section-number\">7. </span>Data Wrangling", "<span class=\"section-number\">8. </span>Basic Programming", "<span class=\"section-number\">9. </span>Statistical theory", "<span class=\"section-number\">10. </span>Introduction to Probability", "<span class=\"section-number\">11. </span>Estimating unknown quantities from a sample", "<span class=\"section-number\">12. </span>Hypothesis Testing", "<span class=\"section-number\">13. </span>Categorical data analysis", "<span class=\"section-number\">14. </span>Comparing Two Means", "<span class=\"section-number\">15. </span>Comparing several means (one-way ANOVA)", 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"conting": 17, "orthodox": 17, "text": 17, "plan": 17, "quick": 17, "refresh": 17, "version": 17, "best": 17, "includ": 17, "term": 17, "prior": 17, "befor": 17, "lie": 17, "while": 7, "syntax": 7, "pattern": 7}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1, "sphinxcontrib.bibtex": 9, "sphinx": 56}}) \ No newline at end of file +Search.setIndex({"docnames": ["01.01-intro", "01.02-studydesign", "02.01-getting_started_with_python", "02.02-more_python_concepts", "03.01-descriptives", "03.02-drawing_graphs", "03.03-pragmatic_matters", "03.04-basic_programming", "04.01-intro-to-probability", "04.02-probability", "04.03-estimation", "04.04-hypothesis-testing", "05.01-chisquare", "05.02-ttest", "05.03-anova", "05.04-regression", "05.05-anova2", "06.01-bayes", "06.02-epilogue", "bibliography", "landingpage"], "filenames": ["01.01-intro.ipynb", "01.02-studydesign.ipynb", "02.01-getting_started_with_python.ipynb", "02.02-more_python_concepts.ipynb", "03.01-descriptives.ipynb", "03.02-drawing_graphs.ipynb", "03.03-pragmatic_matters.ipynb", "03.04-basic_programming.ipynb", "04.01-intro-to-probability.ipynb", "04.02-probability.ipynb", "04.03-estimation.ipynb", "04.04-hypothesis-testing.ipynb", "05.01-chisquare.ipynb", "05.02-ttest.ipynb", "05.03-anova.ipynb", "05.04-regression.ipynb", "05.05-anova2.ipynb", "06.01-bayes.ipynb", "06.02-epilogue.ipynb", "bibliography.md", "landingpage.md"], "titles": ["<span class=\"section-number\">1. </span>Why do we learn statistics?", "<span class=\"section-number\">2. </span>A brief introduction to research design", "<span class=\"section-number\">3. </span>Getting Started with Python", "<span class=\"section-number\">4. </span>More Python Concepts", "<span class=\"section-number\">5. </span>Descriptive statistics", "<span class=\"section-number\">6. </span>Drawing Graphs", "<span class=\"section-number\">7. </span>Data Wrangling", "<span class=\"section-number\">8. </span>Basic Programming", "<span class=\"section-number\">9. </span>Statistical theory", "<span class=\"section-number\">10. </span>Introduction to Probability", "<span class=\"section-number\">11. </span>Estimating unknown quantities from a sample", "<span class=\"section-number\">12. </span>Hypothesis Testing", "<span class=\"section-number\">13. </span>Categorical data analysis", "<span class=\"section-number\">14. </span>Comparing Two Means", "<span class=\"section-number\">15. </span>Comparing several means (one-way ANOVA)", "<span class=\"section-number\">16. </span>Linear regression", "<span class=\"section-number\">17. </span>Factorial ANOVA", "<span class=\"section-number\">18. </span>Bayesian Statistics", "<span class=\"section-number\">19. </span>Epilogue", "<span class=\"section-number\">20. </span>References", "Learning Statistics with Python"], "terms": {"thou": 0, "shalt": 0, "answer": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], "questionnair": [0, 1, 4, 9], "Or": [0, 1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17], "quizz": [0, 12], "upon": [0, 1, 5, 10, 11, 14, 17], "world": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17], "affair": [0, 1, 10], "nor": [0, 1, 9, 11, 16, 17], "complianc": 0, "take": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17], "test": [0, 4, 6, 8, 9, 10, 19], "sit": [0, 1, 3, 4, 6, 7, 9, 10, 11], "With": [0, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15], "statistician": [0, 1, 4, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "commit": [0, 14, 20], "w": [0, 3, 7, 8, 13, 14, 15, 16, 19], "h": [0, 3, 9, 12, 14, 15, 17, 19], "auden": 0, "1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19], "To": [0, 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