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 hundreds 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 0.8691 R Square 0.7554 Adjusted R Square 0.7467 44.4765 Standard Error Observations 30,0000 ANOVA SS MS Significance F 1 171062.9193 171062.9193 86.4759 Regression 0.0000 28 55388.4309 1978.1582 Residual Total 29 226451.3503 Coefficients Standard Emor t Stat P-value Lower 95% Upper 95% 95.0614 26.9183 3.5315 0.0015 150.2009 39,9218 intercept 4.5513 Download 3,7297 9.2992 0.0000 0.4011 2.9082
a) Write down the regression equation.
b) What is the correct interpretation for the slope coefficient?
c) Predict the revenue when the number of downloads is 30,000.
Answer: ______________________________
d) What is the correct interpretation for the coefficient of determination (R2)?
e) The 95% confidence interval estimate for the population slope is (______________ , ______________)
f) Is there sufficient evidence that revenue and the number of downloads are linearly related at a 5% level of significance? Give a reason why.
