Dwww.stat wise edu kar . Chegg Study Guided S: x D ISLRSixth Printing odf DISLR
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Dwww.stat wise edu kar . Chegg Study Guided S: x D ISLRSixth Printing odf DISLR Sixth Printing pdf × G screenshot windows 10 > C www-bcf.usc.edu/.-gareth/ISL/ISLR%20Sixth%20Printing.pdf ISLR Sixth Printing.pdf 140440 point? Both? Explain your answers. 15. This problem involves the Boston data set, which we saw in the lab for this chapter. We will now try to predict per capita crime rate using the other variables in this data set. In other words, per capita crime rate is the response, and the other variables are the predictors (a) For each predictor, fitsuuple linear regression Luodel to predict the response. Describe your results. In which of the models is there a statistically signiticant association between the predictor and the response? Create some plots to back up your assertions. (b) Fit a multiple regression model to predict the response using all of the predictors. Describe your results. For which predictors can we reject the mill hypothesis Ha : = 0? (c) How do your results from (a) compare to your results from (b)? Create a plot displaying the univariate regression coefficients from (a) on the z-axis, and the multiple regression coefficients from (b) on the y-axis. That is, each predictor is displayed as a single point the plot. Its coellicient in a simple linear regres sion model is shown on the z-axis, and its coefficient estimate in the multiple lincur regression model is shown on the y-uxis. (d) Is there evidence of non-linear association between any of the predictors aud the respse? To answer this question, for cauch predictor X, ft a model of the form O Type here to search 8:12 PM E 2/16/2018 4 57Explanation / Answer
15. a)
library(MASS)
All, except chas. Plot each linear regression using “plot(lm)” to see residuals.
15. b)
zn, dis, rad, black, medv
15. C)
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