14a) Astudy analyzed the influence of car speed (variable 1) on the distances (variable 2) needed to a complete stop. The following linear regression model is fitted to examine the relationship. Coefficients: Estimate Std. Error tvalue Pr(>t) (Intercept] -17.5791 6.7584 -2.601 0.0123 3.9324 0.4155 9.464 1.49e-12*** Signif. codes: 0** 0.001 * 0.01 0.05 .0.1 1 Residual standard error: 15.38 on 48 degrees of freedom Multiple R-squared: 0.6511. Adjusted R-squared: 0.6438 -statistic: 89.57 on 1 and 48 DF. p-value: 1.49e-12 Vhat is the response variable? 14(b) What is the explanatory variable (predictor)? 14(CWhen the car speed increases by 10 mph, what is the expected extra stopping distance needed
14a) Astudy analyzed the influence of car speed (variable 1) on the distances (variable 2) needed to a complete stop. The following linear regression model is fitted to examine the relationship. Coefficients: Estimate Std. Error tvalue Pr(>t) (Intercept] -17.5791 6.7584 -2.601 0.0123 3.9324 0.4155 9.464 1.49e-12*** Signif. codes: 0** 0.001 * 0.01 0.05 .0.1 1 Residual standard error: 15.38 on 48 degrees of freedom Multiple R-squared: 0.6511. Adjusted R-squared: 0.6438 -statistic: 89.57 on 1 and 48 DF. p-value: 1.49e-12 Vhat is the response variable? 14(b) What is the explanatory variable (predictor)? 14(CWhen the car speed increases by 10 mph, what is the expected extra stopping distance needed
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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
Transcribed Image Text:14a) Astudy analyzed the influence of car speed (variable 1) on the distances (variable 2) needed to
a complete stop. The following linear regression model is fitted to examine the relationship.
Coefficients:
Estimate Std. Emor t value Pr(>It)
(Intercept) -17.5791 6.7584 -2.601 0.0123"
3.9324 0.4155 9.464 1.49e-12 ***
Signif. codes: 0*** 0.001 *0.01 0.05.0.1 1
Residual standard error: 15.38 on 48 degrees of freedom
Multiple R-squared: 0.6511. Adjusted R-squared: 0.6438
F-statistic: 89.57 on 1 and 48 DF. p-value: 1.49e-12
What is the response variable?
14(b) What is the explanatory variable (predictor)?
14CWhen the car speed increases by 10 mph, what is the expected extra stopping distance needed
to a complete stop?
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