Next, we turn our attention to calculating AIC and BIC. Note that two of these x-variables relate to how long the person has lived at the urban lower altitude. We should then use multiple regression to explore the five-variable model just identified. A five-variable model most likely will be sufficient. If we look at the best six-variable model, we see only minimal changes in these values, and the value of \(S = \sqrt\) increases. The value of R 2 for this model is 63.9% and the value of R 2 adj is 58.4%. The ” X”s to the right side of the display tell us which variables are in the model (look up to the column heading to see the variable name). To interpret the results, we start by noting that the lowest C p value (= 5.5) occurs for the five-variable model that includes the variables Age, Years, fraclife, Weight, and Chin. The results from the best subsets procedure are presented below. X 3 = X 2 / X 1 = fraction of life in urban area The variables in this dataset (where we have omitted the calf skinfold variable from the first time we used this example) are: Recall from Lesson 5 that this dataset consists of variables possibly relating to blood pressures of n = 39 Peruvians who have moved from rural high altitude areas to urban lower altitude areas ( peru.txt). First we will illustrate the “Best Subsets” procedure and a “by hand” calculation of the information criteria from earlier.
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