Kizspy | Question: 70
(Choose 1 answer)
(See picture)
A. (i)
B. (ii)
C. (iii)
D. (iv)
A computer software developer would like to use the number of downloads (inthousands) 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
0.8691
RSquare
0.7554
Adjusted R Square Standard Error
0.7467
44.4765
Observations
30.0000
ANOVA
SS
Regression
1
MS
171062.9193 171062.9193
Residual
28
55388.4309
F
86.4759
Significance F
0.0000
1978.1582
Total
29 226451.3503
Coefficients Standard Error
t Stat
P-value Lower 95%-150.2009 -39.9218
Upper 95%
Intercept
-95.0614
26.9183
-3.5315
0.0015
Download
3.7297
0.4011
9.2992
0.0000
2.9082
4.5513
Which of the following is the correct alternative hypothesis for testing whether there is a linear relationship between revenue and the number of downloads?
(i) H₁=0
(ii) H₁: b₁≠ 0
(iii) H₁: β₁ = 0
(iv) Η₁: β₁ ≠ 0
