1. Referring to Scenario 13-11, which of the following is the correct interpretation for the slope coefficient

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1. Referring to Scenario 13-11, which of the following is the correct interpretation for the slope coefficient?
a) For each decrease of 1 thousand downloads, the expected revenue is estimated to increase by $ 3.7297 thousands.
b) For each increase of 1 thousand downloads, the expected revenue is estimated to increase by $ 3.7297 thousands.
c) For each decrease of 1 thousand dollars in expected revenue, the expected number of downloads is estimated to increase by 3.7297 thousands.
d) For each increase of 1 thousand dollars in expected revenue, the expected number of downloads is estimated to increase by 3.7297 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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