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 30.0000 Observations
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 30.0000 Observations
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
Referring to Scenario 13-11, predict the revenue when the number of downloads is 30 thousands.
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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