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Regression Statistics Multiple R 0.731582322 R Square 0.535212694 Adjusted R Square 0.48158339 Standard Error 3.226367617 Observations 30 ANOVA df SS Regression Residual Total 3 311.654352 MS 103.884784 9.979855224 F Significance F 0.000148979 26 270.645648 10.409448 29 582.3 Intercept -6.514962018 Age 0.211845838 Gender Numeric 1.862911672 Employee Status numeric 4.741019749 Coefficients Standard Error t Stat P-value 2.800251946 -2.326562804 0.028049483 0.051373979 4.123601872 0.000338465 1.216001581 1.531997738 0.137601494 1.632595081 2.903977724 0.007420914 1.385172498 Upper 95% Lower 95% Lower 95.0% Upper 95.0% -12.27096233 -0.758961708 -12.27096233 -0.758961708 0.106245111 0.317446565 0.106245111 0.317446565 -0.636615376 4.36243872 -0.636615376 4.36243872 8.096867 1.385172498 8.096867

Regression Statistics Multiple R 0.731582322 R Square 0.535212694 Adjusted R Square 0.48158339 Standard Error 3.226367617 Observations 30 ANOVA df SS Regression Residual Total 3 311.654352 MS 103.884784 9.979855224 F Significance F 0.000148979 26 270.645648 10.409448 29 582.3 Intercept -6.514962018 Age 0.211845838 Gender Numeric 1.862911672 Employee Status numeric 4.741019749 Coefficients Standard Error t Stat P-value 2.800251946 -2.326562804 0.028049483 0.051373979 4.123601872 0.000338465 1.216001581 1.531997738 0.137601494 1.632595081 2.903977724 0.007420914 1.385172498 Upper 95% Lower 95% Lower 95.0% Upper 95.0% -12.27096233 -0.758961708 -12.27096233 -0.758961708 0.106245111 0.317446565 0.106245111 0.317446565 -0.636615376 4.36243872 -0.636615376 4.36243872 8.096867 1.385172498 8.096867

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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Question
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I would like to get help to know how to make a report of the multiple regression analysis I performed about 30 employees salaries. it should include a description of the predictice model, pre-analysis, regression analysis, interpretation of the relevant findings and a conclusion with recommendations. thank you

Regression Statistics Multiple R 0.731582322 R Square 0.535212694 Adjusted R Square 0.48158339 Standard Error 3.226367617 Observations 30 ANOVA df SS Regression Residual Total 3 311.654352 MS 103.884784 9.979855224 F Significance F 0.000148979 26 270.645648 10.409448 29 582.3 Intercept -6.514962018 Age 0.211845838 Gender Numeric 1.862911672 Employee Status numeric 4.741019749 Coefficients Standard Error t Stat P-value 2.800251946 -2.326562804 0.028049483 0.051373979 4.123601872 0.000338465 1.216001581 1.531997738 0.137601494 1.632595081 2.903977724 0.007420914 1.385172498 Upper 95% Lower 95% Lower 95.0% Upper 95.0% -12.27096233 -0.758961708 -12.27096233 -0.758961708 0.106245111 0.317446565 0.106245111 0.317446565 -0.636615376 4.36243872 -0.636615376 4.36243872 8.096867 1.385172498 8.096867
Transcribed Image Text:Regression Statistics Multiple R 0.731582322 R Square 0.535212694 Adjusted R Square 0.48158339 Standard Error 3.226367617 Observations 30 ANOVA df SS Regression Residual Total 3 311.654352 MS 103.884784 9.979855224 F Significance F 0.000148979 26 270.645648 10.409448 29 582.3 Intercept -6.514962018 Age 0.211845838 Gender Numeric 1.862911672 Employee Status numeric 4.741019749 Coefficients Standard Error t Stat P-value 2.800251946 -2.326562804 0.028049483 0.051373979 4.123601872 0.000338465 1.216001581 1.531997738 0.137601494 1.632595081 2.903977724 0.007420914 1.385172498 Upper 95% Lower 95% Lower 95.0% Upper 95.0% -12.27096233 -0.758961708 -12.27096233 -0.758961708 0.106245111 0.317446565 0.106245111 0.317446565 -0.636615376 4.36243872 -0.636615376 4.36243872 8.096867 1.385172498 8.096867
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