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.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter3: Straight Lines And Linear Functions

Section3.4: Linear Regression

Problem 12SBE: Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4

See similar textbooks

Related questions

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

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.

Expert Solution

Trending now

This is a popular solution!

Step by step

Solved in 4 steps

SEE SOLUTION Check out a sample Q&A here