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what is the critical value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance

what is the critical value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance

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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Referring to Scenario 13-11, what is the critical value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance

SCENARIO 13-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.8691 0.7554 0.7467 44.4765 Observations 30.0000 ANOVA Significance F 0.0000 df MS Regression 1. 171062.9193 171062.9193 86.4759 Residual 28 55388.4309 1978. 1582 Total 29 226451.3503 Intercept Download Coefficients -95.0614 Standard Error 26.9183 t Stat -3.5315 9.2992 P-value 0.0015 Lower 95% -150.2009 2.9082 Upper 95% -39.9218 4.5513 3.7297 0.4011 0.0000 Dowrlood Residual Plot Normal Probability Plot 120 120 100 100 80 80 60 60 40 40 -20 40 60 80 -80 -300 -100 D.00 20.00 40.00 60.00 B0.00 100.00 120.00 -2 -1 Download Z Valur Reciduals SIerpisay
Transcribed Image Text:SCENARIO 13-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.8691 0.7554 0.7467 44.4765 Observations 30.0000 ANOVA Significance F 0.0000 df MS Regression 1. 171062.9193 171062.9193 86.4759 Residual 28 55388.4309 1978. 1582 Total 29 226451.3503 Intercept Download Coefficients -95.0614 Standard Error 26.9183 t Stat -3.5315 9.2992 P-value 0.0015 Lower 95% -150.2009 2.9082 Upper 95% -39.9218 4.5513 3.7297 0.4011 0.0000 Dowrlood Residual Plot Normal Probability Plot 120 120 100 100 80 80 60 60 40 40 -20 40 60 80 -80 -300 -100 D.00 20.00 40.00 60.00 B0.00 100.00 120.00 -2 -1 Download Z Valur Reciduals SIerpisay
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