added rtfm1 and rtfm2 to data

Chapters/.ipynb_checkpoints/05.04-regression-checkpoint.ipynb

@@ -6541,7 +6541,7 @@
    "source": [
     "BIC is also smaller for `MO` than for `M1`, so based on both AIC and BIC, it looks like Model 0 is the better choice.\n",
     "\n",
-    "A somewhat different approach to the problem comes out of the hypothesis testing framework. Suppose you have two regression models, where one of them (Model 0) contains a *subset* of the predictors from the other one (Model 1). That is, Model 1 contains all of the predictors included in Model 0, plus one or more additional predictors. When this happens we say that Model 0 is **_nested_** within Model 1, or possibly that Model 0 is a **_submodel_** of Model 1. Regardless of the terminology what this means is that we can think of Model 0 as a null hypothesis and Model 1 as an alternative hypothesis. And in fact we can construct an $F$ test for this in a fairly straightforward fashion. We can fit both models to the data and obtain a residual sum of squares for both models. I'll denote these as SS$_{res}^{(0)}$ and SS$_{res}^{(1)}$ respectively. The superscripting here just indicates which model we're talking about.  Then our $F$ statistic is\n",
+    "A somewhat different approach to the problem comes out of the hypothesis testing framework. Suppose you have two regression models, where one of them (Model 0) contains a *subset* of the predictors from the other one (Model 1). That is, Model 1 contains all of the predictors included in Model 0, plus one or more additional predictors. When this happens we say that Model 0 is **_nested_** within Model 1, or possibly that Model 0 is a **_submodel_** of Model 1. Regardless of the terminology, what this means is that we can think of Model 0 as a null hypothesis and Model 1 as an alternative hypothesis. And in fact we can construct an $F$ test for this in a fairly straightforward fashion. We can fit both models to the data and obtain a residual sum of squares for both models. I'll denote these as SS$_{res}^{(0)}$ and SS$_{res}^{(1)}$ respectively. The superscripting here just indicates which model we're talking about.  Then our $F$ statistic is\n",
     "\n",
     "$$\n",
     "F = \\frac{(\\mbox{SS}_{res}^{(0)} - \\mbox{SS}_{res}^{(1)})/k}{(\\mbox{SS}_{res}^{(1)})/(N-p-1)}\n",