An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
13th Edition
ISBN: 9781461471370
Author: Gareth James
Publisher: SPRINGER NATURE CUSTOMER SERVICE
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Chapter 2, Problem 9E

a.

Explanation of Solution

Predictors

  • Name is qualitative, the rest are quantitative.
  • However, looking at summary(), it is notic...

b.

Explanation of Solution

Range of predictor

  • The range of each quantitative pred...

c.

Explanation of Solution

Mean and standard deviation of predictor

  • Using signif() function, it can be round to two significant digits...

d.

Explanation of Solution

Range,median and standard deviation of predictor

  • Using round() function, it rounds to two decimal places rather than two significant digits...

e.

Explanation of Solution

Simple linear regression

  • It is easy to see that if xi is replac...

f.

Explanation of Solution

Predictors

  • After plotting predictors graphically, it will be

    library(pheatmap)

    pheatmap(t(scale(as...

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Students have asked these similar questions
We created some models for a dataset and, for each model we computed its R2 score. The results are presented in the table below: Model m1 m2 m3 m4 m5 R2 0.85 0.76 0.87 0.68 0.79 What model should we use from the ones presented in the table? Justify your answer. Answer:
Draw a QQ (quantile-quantile) plot for the built-in data set, islands, to assess the normality of the observations. Is the data set well-modeled by a normal distribution?
If we add more independent variables into the model:   A. The adjusted R2 value will increase.   B. The R2 value will increase.   C. The R2 value will decrease if the variables we are adding into the model should not be there.   D. The R2 will be biased.

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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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