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. Referring to Scenario 13-11, predict the revenue when the number of downloads is 30 thousands.

. Referring to Scenario 13-11, predict the revenue when the number of downloads is 30 thousands.

College Algebra
College Algebra
7th Edition
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:James Stewart, Lothar Redlin, Saleem Watson
Chapter1: Equations And Graphs
Section: Chapter Questions
Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...
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2. Referring to Scenario 13-11, predict the revenue when the number of downloads is 30 thousands.

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 df MS Significance F 0.0000 F Regression 171062.9193 171062.9193 86.4759 Residual 28 55388.4309 1978. 1582 Total 29 226451.3503 Standard Error 26.9183 t Stat -3.5315 Lower 95% Upper 95% Coefficients P-value Intercept -95.0614 0.0015 -150.2009 -39.9218 Download 3.7297 0.4011 9.2992 0.0000 2.9082 4.5513 Download Residual Plot Normal Probability Plot 120 120 100 100 80 80 60 60 40 40 20 20 -20 -20 40 -40 -60 -60 -80 -80 -100 -100 0.00 20.00 40.00 60.00 80.00 100.00 120.00 -3 -1 Download Z Value 2. 2. sjerpisay Residuals
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 df MS Significance F 0.0000 F Regression 171062.9193 171062.9193 86.4759 Residual 28 55388.4309 1978. 1582 Total 29 226451.3503 Standard Error 26.9183 t Stat -3.5315 Lower 95% Upper 95% Coefficients P-value Intercept -95.0614 0.0015 -150.2009 -39.9218 Download 3.7297 0.4011 9.2992 0.0000 2.9082 4.5513 Download Residual Plot Normal Probability Plot 120 120 100 100 80 80 60 60 40 40 20 20 -20 -20 40 -40 -60 -60 -80 -80 -100 -100 0.00 20.00 40.00 60.00 80.00 100.00 120.00 -3 -1 Download Z Value 2. 2. sjerpisay Residuals
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