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 What is the test-statistic is used to test whether at least one of the explanatory variables is a significant predictor of the response variable? 2.259 -0.332 0.5731 0.884 -0.946
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 What is the test-statistic is used to test whether at least one of the explanatory variables is a significant predictor of the response variable? 2.259 -0.332 0.5731 0.884 -0.946
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
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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 |
What is the test-statistic is used to test whether at least one of the explanatory variables is a significant predictor of the response variable?
2.259
-0.332
0.5731
0.884
-0.946
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