which of the following is the correct interpretation for the slope coefficient?

MATLAB: An Introduction with Applications
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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
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
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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