ceferring to Table 12-11, which of the following is the correct alternative hypothesis for testing whether there is a linear relationship between revenue and he number of downloads? Seleccione una: O A. H: B1 = 0 O B. H1: b1 = 0 H: b1 0 O D. H: B1 = 0

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Normal Probability Plot
120
100
80
60
40
20
-20
..... *****
-40
60
-80
-100
-3
-1
Z Value
Referring to Table 12-11, which of the following is the correct alternative hypothesis for testing whether there is a linear relationship between revenue and
the number of downloads?
Seleccione una:
O A.
H: B1 = 0
O B.
H1: b1 = 0
H: b1 # 0
O D.
H1: B1 = 0
Transcribed Image Text:Normal Probability Plot 120 100 80 60 40 20 -20 ..... ***** -40 60 -80 -100 -3 -1 Z Value Referring to Table 12-11, which of the following is the correct alternative hypothesis for testing whether there is a linear relationship between revenue and the number of downloads? Seleccione una: O A. H: B1 = 0 O B. H1: b1 = 0 H: b1 # 0 O D. H1: B1 = 0
TABLE 12-11
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 Eror
0.8691
0,7554
0.7467
44.4765
Observations
30.0000
ANOVA
df
Significance F
0.0000
MS
SS
171062.9193 171062.9193
55388.4309
226451.3503
F
86.4759
Regression
Residual
1
28
1978. 1582
Total
29
Coefficients
95.0614
Standard Eror
26.9183
t Stat
3.5315
Lower 95%
-150, 2009
Upper 95%
39.9218
4.5513
P-value
Intercept
Download
0.0015
3.7297
0.4011
9.2992
0.0000
2.9082
Download Residual Plot
120
100
80
60
40
20
-20
40
-60
-80
-100
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Download
Normal Probability Plot
120
100
80
60
40
20
sjenpisay
genpys
Transcribed Image Text:TABLE 12-11 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 Eror 0.8691 0,7554 0.7467 44.4765 Observations 30.0000 ANOVA df Significance F 0.0000 MS SS 171062.9193 171062.9193 55388.4309 226451.3503 F 86.4759 Regression Residual 1 28 1978. 1582 Total 29 Coefficients 95.0614 Standard Eror 26.9183 t Stat 3.5315 Lower 95% -150, 2009 Upper 95% 39.9218 4.5513 P-value Intercept Download 0.0015 3.7297 0.4011 9.2992 0.0000 2.9082 Download Residual Plot 120 100 80 60 40 20 -20 40 -60 -80 -100 0.00 20.00 40.00 60.00 80.00 100.00 120.00 Download Normal Probability Plot 120 100 80 60 40 20 sjenpisay genpys
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