Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables: shoe size – in inches age – in years height – in inches forearm length – in inches Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows: > summary(lm(shoesize ~ age + height + forearm, data = measures)) Coefficients: (Intercept) age height forearm Estimate 10.14882 0.06045 -0.02108 -0.06479 Std. Error 4.49245 0.06838 0.06350 0.06847 t value 2.259 0.884 -0.332 -0.946 Pr(>|t|) 0.0264 0.3792 0.7408 0.3467 Residual standard error: 1.719 on 85 degrees of freedom Multiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342 Which of the following is the correct conclusion for the F-test that was performed? There is strong evidence to indicate that age, height, and forearm length are all significant predictors of shoe size. There is not enough evidence to indicate that age, height, and forearm length are significant predictors of shoe size. There is not enough evidence to indicate that at least one of age, height, or forearm length are significant predictors of shoe size. There is strong evidence to indicate that at least one of age, height, or forearm length are significant predictors of shoe size.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

Suppose that researchers obtain a random sample of adults ages 18 – 40 and collect data on the following variables:

shoe size – in inches

age – in years

height – in inches

forearm length – in inches

Suppose further that a multiple linear regression model is fit to the resulting data set using R Studio and that the following output is obtained from it. Use this output to answer the question that follows:

> summary(lm(shoesize ~ age + height + forearm, data = measures))
Coefficients:
(Intercept) age height forearm	Estimate 10.14882 0.06045 -0.02108 -0.06479	Std. Error 4.49245 0.06838 0.06350 0.06847	t value 2.259 0.884 -0.332 -0.946	Pr(>\|t\|) 0.0264 0.3792 0.7408 0.3467

Residual standard error: 1.719 on 85 degrees of freedom Multiple R-squared: 0.01983, Adjusted R-squared: -0.01477 F-statistic: 0.5731 on 3 and 85 DF, p-value: 0.6342