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b. Use part (a) to perform the F test [show the formula used to calculate F-statistic value] for testing the significance of the straight-line model (Use a = 0.10). Interpret your decision.

b. Use part (a) to perform the F test [show the formula used to calculate F-statistic value] for testing the significance of the straight-line model (Use a = 0.10). Interpret your decision.

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
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Answer part B using information from part a!

**b. [Redacted] Use part (a) to perform the F test [show the formula used to calculate F-statistic value] for testing the significance of the straight-line model (Use α = 0.10). Interpret your decision.**
Transcribed Image Text:**b. [Redacted] Use part (a) to perform the F test [show the formula used to calculate F-statistic value] for testing the significance of the straight-line model (Use α = 0.10). Interpret your decision.**
# Regression Analysis using SAS Output ## Edited SAS Output (PROC REG) for Problem 6 ### Regression of ATST (Y) on AGE (X) #### Summary of Results: - **Root MSE**: 13.15238 - **R-Square**: 0.9054 #### Parameter Estimates | Variable | DF | Parameter Estimate | Standard Error | t Value | |------------|----|--------------------|----------------|---------| | Intercept | 1 | 646.48334 | 12.91773 | 50.05 | | X | 1 | -14.04105 | 1.36812 | -10.26 | ## Additional Information - **Sample size (\(n\))**: 13 - **Variance of residuals (\(S^2_{Y|X}\))**: 172.985 - **Sum of squares total (SSY)**: 20,123.382 ### Task: a. Construct the ANOVA table for the regression of ATST on AGE. --- This output presents the results of a regression analysis conducted to predict the variable ATST using AGE as the predictor. Key metrics include the root mean square error (Root MSE) and the coefficient of determination (R-Square), both critical for assessing the model's accuracy and explanatory power. The parameter estimates indicate the contribution of each predictor to the model, with statistical significance assessed by the t values.
Transcribed Image Text:# Regression Analysis using SAS Output ## Edited SAS Output (PROC REG) for Problem 6 ### Regression of ATST (Y) on AGE (X) #### Summary of Results: - **Root MSE**: 13.15238 - **R-Square**: 0.9054 #### Parameter Estimates | Variable | DF | Parameter Estimate | Standard Error | t Value | |------------|----|--------------------|----------------|---------| | Intercept | 1 | 646.48334 | 12.91773 | 50.05 | | X | 1 | -14.04105 | 1.36812 | -10.26 | ## Additional Information - **Sample size (\(n\))**: 13 - **Variance of residuals (\(S^2_{Y|X}\))**: 172.985 - **Sum of squares total (SSY)**: 20,123.382 ### Task: a. Construct the ANOVA table for the regression of ATST on AGE. --- This output presents the results of a regression analysis conducted to predict the variable ATST using AGE as the predictor. Key metrics include the root mean square error (Root MSE) and the coefficient of determination (R-Square), both critical for assessing the model's accuracy and explanatory power. The parameter estimates indicate the contribution of each predictor to the model, with statistical significance assessed by the t values.
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