Regression Statistics 0.994215326 0.988464115 0.985580144 3.96139482 Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept X Variable 1 df 6 SS MS F Significance F 1 5378.562738 5378.563 342.7441 5.00969E-05 4 62.77059569 15.69265 5 5441.33333 Coefficients Standard Errort Stat P-value Lower 95% 91.99493029 4.936028654 18.63744 4.88E-05 78.2903177 1.279087452 0.069090009 18.51335 5.01E-05 1.087262835 Upper 95% 105.699542 1.4709120
Regression Statistics 0.994215326 0.988464115 0.985580144 3.96139482 Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept X Variable 1 df 6 SS MS F Significance F 1 5378.562738 5378.563 342.7441 5.00969E-05 4 62.77059569 15.69265 5 5441.33333 Coefficients Standard Errort Stat P-value Lower 95% 91.99493029 4.936028654 18.63744 4.88E-05 78.2903177 1.279087452 0.069090009 18.51335 5.01E-05 1.087262835 Upper 95% 105.699542 1.4709120
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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Question
The following table is the partial reproduction of Regression routine output in Microsoft Excel
relating the total cost of production TC to the level of output Q.
Use the information in the table and answer the following:
- a) Report the estimated equation.
- b) Evaluate the regression model
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