Homework Help is Here – Start Your Trial Now!
Math
Statistics
## Summary Output of Regression Analysis ### Regression Statistics - **Multiple R:** 0.856730669 - **R Square:** 0.733987439 - **Adjusted R Square:** 0.687044045 - **Standard Error:** 2.967605917 - **Observations:** 21 ### ANOVA | Source | df | SS | MS | F | Significance F | |---------------|-----|--------------|--------------|-------------|----------------------| | Regression | 3 | 413.0930237 | 137.6976746 | 15.63558552 | 3.87427E-05 | | Residual | 17 | 149.7136429 | 8.806684879 | | | | Total | 20 | 562.8066667 | | | | ### Coefficients | Predictor | Coefficients | Standard Error | t Stat | P-value | |--------------------|--------------|----------------|-------------|----------------------| | Intercept | 25.41936066 | 4.338034616 | 5.859649107 | 1.89327E-05 | | hp | -0.161091493 | 0.035506341 | -4.536978136| 0.000291759 | | curbwt | -0.000302296 | 0.002323767 | -0.135390557| 0.893893295 | | hp*curbwt | 2.8177E-05 | 9.4596E-06 | 2.978662513 | 0.008429352 | **Explanation of ANOVA Table:** - **Regression**: Represents the variability explained by the model with 3 degrees of freedom. - **Residual**: Represents the variability not explained by the model with 17 degrees of freedom. - **Total**: Sum of squares representing total variability with 20 degrees of freedom. **Coefficients Overview:** - **Intercept**: The estimated value when all predictors are zero. - **hp**: A unit increase in hp results in a decrease in the outcome by approximately 0.161. - **curbwt

## Summary Output of Regression Analysis ### Regression Statistics - **Multiple R:** 0.856730669 - **R Square:** 0.733987439 - **Adjusted R Square:** 0.687044045 - **Standard Error:** 2.967605917 - **Observations:** 21 ### ANOVA | Source | df | SS | MS | F | Significance F | |---------------|-----|--------------|--------------|-------------|----------------------| | Regression | 3 | 413.0930237 | 137.6976746 | 15.63558552 | 3.87427E-05 | | Residual | 17 | 149.7136429 | 8.806684879 | | | | Total | 20 | 562.8066667 | | | | ### Coefficients | Predictor | Coefficients | Standard Error | t Stat | P-value | |--------------------|--------------|----------------|-------------|----------------------| | Intercept | 25.41936066 | 4.338034616 | 5.859649107 | 1.89327E-05 | | hp | -0.161091493 | 0.035506341 | -4.536978136| 0.000291759 | | curbwt | -0.000302296 | 0.002323767 | -0.135390557| 0.893893295 | | hp*curbwt | 2.8177E-05 | 9.4596E-06 | 2.978662513 | 0.008429352 | **Explanation of ANOVA Table:** - **Regression**: Represents the variability explained by the model with 3 degrees of freedom. - **Residual**: Represents the variability not explained by the model with 17 degrees of freedom. - **Total**: Sum of squares representing total variability with 20 degrees of freedom. **Coefficients Overview:** - **Intercept**: The estimated value when all predictors are zero. - **hp**: A unit increase in hp results in a decrease in the outcome by approximately 0.161. - **curbwt

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
See similar textbooks
icon
Related questions
Question

Acceleration Time. Suppose the number of seconds required to accelerate to 60 mph depends on both a car’s weight, its horsepower, and an interaction term between the two variables. A partial computer output from a regression analysis using Excel’s Regression tool follows.

 

Interpret the partial regression coefficients and discuss the results of each significance test.

 

## Summary Output of Regression Analysis ### Regression Statistics - **Multiple R:** 0.856730669 - **R Square:** 0.733987439 - **Adjusted R Square:** 0.687044045 - **Standard Error:** 2.967605917 - **Observations:** 21 ### ANOVA | Source | df | SS | MS | F | Significance F | |---------------|-----|--------------|--------------|-------------|----------------------| | Regression | 3 | 413.0930237 | 137.6976746 | 15.63558552 | 3.87427E-05 | | Residual | 17 | 149.7136429 | 8.806684879 | | | | Total | 20 | 562.8066667 | | | | ### Coefficients | Predictor | Coefficients | Standard Error | t Stat | P-value | |--------------------|--------------|----------------|-------------|----------------------| | Intercept | 25.41936066 | 4.338034616 | 5.859649107 | 1.89327E-05 | | hp | -0.161091493 | 0.035506341 | -4.536978136| 0.000291759 | | curbwt | -0.000302296 | 0.002323767 | -0.135390557| 0.893893295 | | hp*curbwt | 2.8177E-05 | 9.4596E-06 | 2.978662513 | 0.008429352 | **Explanation of ANOVA Table:** - **Regression**: Represents the variability explained by the model with 3 degrees of freedom. - **Residual**: Represents the variability not explained by the model with 17 degrees of freedom. - **Total**: Sum of squares representing total variability with 20 degrees of freedom. **Coefficients Overview:** - **Intercept**: The estimated value when all predictors are zero. - **hp**: A unit increase in hp results in a decrease in the outcome by approximately 0.161. - **curbwt
Transcribed Image Text:## Summary Output of Regression Analysis ### Regression Statistics - **Multiple R:** 0.856730669 - **R Square:** 0.733987439 - **Adjusted R Square:** 0.687044045 - **Standard Error:** 2.967605917 - **Observations:** 21 ### ANOVA | Source | df | SS | MS | F | Significance F | |---------------|-----|--------------|--------------|-------------|----------------------| | Regression | 3 | 413.0930237 | 137.6976746 | 15.63558552 | 3.87427E-05 | | Residual | 17 | 149.7136429 | 8.806684879 | | | | Total | 20 | 562.8066667 | | | | ### Coefficients | Predictor | Coefficients | Standard Error | t Stat | P-value | |--------------------|--------------|----------------|-------------|----------------------| | Intercept | 25.41936066 | 4.338034616 | 5.859649107 | 1.89327E-05 | | hp | -0.161091493 | 0.035506341 | -4.536978136| 0.000291759 | | curbwt | -0.000302296 | 0.002323767 | -0.135390557| 0.893893295 | | hp*curbwt | 2.8177E-05 | 9.4596E-06 | 2.978662513 | 0.008429352 | **Explanation of ANOVA Table:** - **Regression**: Represents the variability explained by the model with 3 degrees of freedom. - **Residual**: Represents the variability not explained by the model with 17 degrees of freedom. - **Total**: Sum of squares representing total variability with 20 degrees of freedom. **Coefficients Overview:** - **Intercept**: The estimated value when all predictors are zero. - **hp**: A unit increase in hp results in a decrease in the outcome by approximately 0.161. - **curbwt
Expert Solution
steps

Step by step

Solved in 2 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Similar questions
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman
SEE MORE TEXTBOOKS