Cars.com would like to use simple linear regression to predict the selling price of a used car, in thousands of dollars, based on the age of the car in years. A random sample of used cars was selected and the result of the regression analysis is shown below. SUMMARY OUTPUT Regression Statistics Multiple R 0.7885 R Square 0.6217 Adjusted R Square 0.5461 Standard Error 1.4528 Observations ANOVA df Significance F MS Regression 1 17.3464 17.3464 8.2182 0.0351 Residual 5. 10.5536 2.1107 Total 27.9 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept Age 11.6881 1.2403 9.4237 0.0002 8.4999 14.8764 -0.5072 0.1769 -2.8667 0.0351 -0.9620 -0.0524 Based on the given information, thep value for testing if slope of true line is different from zero is (Keep all the decimal places) A and the value of the coefficient of determination for this model is (Keep all the decimal places)

College Algebra
10th Edition
ISBN:9781337282291
Author:Ron Larson
Publisher:Ron Larson
Chapter3: Polynomial Functions
Section3.5: Mathematical Modeling And Variation
Problem 2ECP
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Cars.com would like to use simple linear regression to predict the selling price of a used car, in thousands of dollars, based on the age of the car in years. A random sample of used cars was selected and the result of the regression analysis is shown below. 

 

Based on the given information, the p value for testing if slope of true line is different from zero is (Keep all the decimal places)

 
 
and the value of the coefficient of determination for this model is (Keep all the decimal places)
 
 
 

 

Cars.com would like to use simple linear regression to predict the selling price of a
used car, in thousands of dollars, based on the age of the car in years. A random
sample of used cars was selected and the result of the regression analysis is shown
below.
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.7885
R Square
0.6217
Adjusted R Square
0.5461
Standard Error
1.4528
Observations
ANOVA
df
Significance F
MS
Regression
1
17.3464 17.3464 8.2182
0.0351
Residual
5.
10.5536 2.1107
Total
27.9
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept
Age
11.6881
1.2403 9.4237 0.0002
8.4999
14.8764
-0.5072
0.1769 -2.8667 0.0351
-0.9620
-0.0524
Based on the given information, thep value for testing if slope of true line is
different from zero is (Keep all the decimal places)
A and the value of the coefficient of
determination for this model is (Keep all the decimal places)
Transcribed Image Text:Cars.com would like to use simple linear regression to predict the selling price of a used car, in thousands of dollars, based on the age of the car in years. A random sample of used cars was selected and the result of the regression analysis is shown below. SUMMARY OUTPUT Regression Statistics Multiple R 0.7885 R Square 0.6217 Adjusted R Square 0.5461 Standard Error 1.4528 Observations ANOVA df Significance F MS Regression 1 17.3464 17.3464 8.2182 0.0351 Residual 5. 10.5536 2.1107 Total 27.9 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept Age 11.6881 1.2403 9.4237 0.0002 8.4999 14.8764 -0.5072 0.1769 -2.8667 0.0351 -0.9620 -0.0524 Based on the given information, thep value for testing if slope of true line is different from zero is (Keep all the decimal places) A and the value of the coefficient of determination for this model is (Keep all the decimal places)
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