Referring to Scenario 13-11, which of the following is the correct interpretation for the coefficient of determination? a) 74.67% of the variation in revenue can be explained by the variation in the number of downloads. b) 75.54% of the variation in revenue can be explained by the variation in the number of downloads. c) 74.67% of the variation in the number of downloads can be explained by the variation in revenue. d) 75.54% of the variation in the number of downloads can be explained by the variation in revenue.
Referring to Scenario 13-11, which of the following is the correct interpretation for the coefficient of determination? a) 74.67% of the variation in revenue can be explained by the variation in the number of downloads. b) 75.54% of the variation in revenue can be explained by the variation in the number of downloads. c) 74.67% of the variation in the number of downloads can be explained by the variation in revenue. d) 75.54% of the variation in the number of downloads can be explained by the variation in revenue.
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
3. Referring to Scenario 13-11, which of the following is the correct interpretation for the coefficient of determination?
a) 74.67% of the variation in revenue can be explained by the variation in the number of downloads.
b) 75.54% of the variation in revenue can be explained by the variation in the number of downloads.
c) 74.67% of the variation in the number of downloads can be explained by the variation in revenue.
d) 75.54% of the variation in the number of downloads can be explained by the variation in revenue.
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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