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Regression Analysis of computer Software

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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 not complete 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 0.7554
Adjusted R Square 0.7467
Standard Error
Observations 30.0000

ANOVA
df SS MS F Significance F
Regression 1 171062.9193 171062.9193 86.4759
Residual 1978.1582
Total 29 226451.3503

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept -95.0614 26.9183 -3.5315 0.0015 -150.2009 -39.9218
Download 3.7297 0.4011

1) Referring to Table 13-11, what is the standard deviation around the regression line?
2) Referring to Table 13-11, conduct t-test whether there is a linear relationship between revenue and the number of downloads? Use p-value and alpha =.05
3) Referring to Table 13-11, what are, respectively, the lower and upper limits of the 95% confidence interval estimate for population slope?

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Solution Summary

Regression analysis of computer software is examined.

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1. standard deviation around the regression line(standard error of the estimate): sqrt(1978.1582)=44.47649042

2. Ho: r=0
Ha: r is not equal to 0.
this is a two tailed t test.
...

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