what is the critical value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance
what is the critical value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.6: Regression And Median-fit Lines
Problem 4GP
Related questions
Question
Referring to Scenario 13-11, what is the critical value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance

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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