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 OC. 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.
H: b1 = 0
OC.
H: b1 + 0
OD.
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. H: b1 = 0 OC. H: b1 + 0 OD. 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
Significance F
0.0000
df
SS
171062.9193 171062.9193
55388.4309
226451.3503
MS
Regression
Residual
1
86.4759
28
1978. 1582
Total
29
Intercept
Download
Coefficients
95.0614
3.7297
Standard Eror
26.9183
0.4011
t Stat
3.5315
9.2992
P-value
0.0015
0.0000
Lower 95%
-150. 2009
Upper 95%
39.9218
2.9082
4.5513
Download Residual Plot
120
100
80
60
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20
-20
-40
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-80
-100
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Download
Normal Probability Plot
120
100
80
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20
Residuals
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 Significance F 0.0000 df SS 171062.9193 171062.9193 55388.4309 226451.3503 MS Regression Residual 1 86.4759 28 1978. 1582 Total 29 Intercept Download Coefficients 95.0614 3.7297 Standard Eror 26.9183 0.4011 t Stat 3.5315 9.2992 P-value 0.0015 0.0000 Lower 95% -150. 2009 Upper 95% 39.9218 2.9082 4.5513 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 Residuals
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