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

Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
X Variable 1
0.994215326
0.988464115
0.985580144
3.96139482
df
6
SS
MS
F
Significance F
1 5378.562738 5378.563 342.7441 5.00969E-05
4 62.77059569 15.69265
5 5441.333333
Coefficients Standard Error t 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%
105.6995429
1.47091207

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Step 1: Regression equation is defined

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