TABLE 12-9 It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Microsoft Excel output for predicting starting salary (Y) using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.8857 0.7845 0.7801 1.3704 Observations 51 ANOVA df MS F Significance F SS 335.0472 335.0473 178.3859 Regression Residual 1 1.8782 Total 50 427.0798 Standard t Stat P-value Lower 95% Upper 95% Coefficients Error -1.8940 0.4018 -4.7134 2.05IE-05 -2.7015 -1.0865 Intercept Hours 0,9795 0.0733 13.3561 5.944E-18 0.8321 1.1269 Note: 2.051E-05 = 2.051 • 10-0.5 and 5.944E-18 = 5.944 10-18 Referring to Table 12-9, the 90% confidence interval for the average change in SALARY (in thousands of dollars) as a result of spending an extra hour per day studying is Seleccione una: O A. wider than [-2.70159, -1.08654]. OB. narrower than [-2.70159, -1.08654]. Oc. wider than [0.8321927, 1.12697]. D. narrower than [0.8321927, 1.12697].
TABLE 12-9 It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Microsoft Excel output for predicting starting salary (Y) using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.8857 0.7845 0.7801 1.3704 Observations 51 ANOVA df MS F Significance F SS 335.0472 335.0473 178.3859 Regression Residual 1 1.8782 Total 50 427.0798 Standard t Stat P-value Lower 95% Upper 95% Coefficients Error -1.8940 0.4018 -4.7134 2.05IE-05 -2.7015 -1.0865 Intercept Hours 0,9795 0.0733 13.3561 5.944E-18 0.8321 1.1269 Note: 2.051E-05 = 2.051 • 10-0.5 and 5.944E-18 = 5.944 10-18 Referring to Table 12-9, the 90% confidence interval for the average change in SALARY (in thousands of dollars) as a result of spending an extra hour per day studying is Seleccione una: O A. wider than [-2.70159, -1.08654]. OB. narrower than [-2.70159, -1.08654]. Oc. wider than [0.8321927, 1.12697]. D. narrower than [0.8321927, 1.12697].
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![TABLE 12-9
It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear
relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Microsoft Excel output for
predicting starting salary (Y using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown.
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
0.8857
0.7845
0.7801
1.3704
Observations
51
ANOVA
df
SS
MS
F
Significance F
Regression
1
335.0472 335.0473
178.3859
Residual
1.8782
Total
50
427.0798
Standard
Upper 95%
-1.0865
Coefficients
Error
t Stat
P-value
Lower 95%
-1.8940
0.4018
-4.7134 2.051E-05
-2.7015
Intercept
Hours
0.9795
0.0733
13.3561
5.944E-18
0.8321
1.1269
Note: 2.051E-05 = 2.051 * 10-0.5 and 5.944E-18 = 5.944 * 10-18
Referring to Table 12-9, the 90% confidence interval for the average change in SALARY (in thousands of dollars) as a result of spending an extra hour per
day studying is
Seleccione una:
O A.
wider than [-2.70159, -1.08654].
O B.
narrower than [-2.70159, -1.08654].
wider than [0.8321927, 1.12697].
OD.
narrower than [0.8321927, 1.12697].](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F00fbdba4-42bc-4346-9411-04714e88dad2%2F42875606-5fee-4a37-9e7e-c4fef5500b2d%2Fedwnvs_processed.jpeg&w=3840&q=75)
Transcribed Image Text:TABLE 12-9
It is believed that, the average numbers of hours spent studying per day (HOURS) during undergraduate education should have a positive linear
relationship with the starting salary (SALARY, measured in thousands of dollars per month) after graduation. Given below is the Microsoft Excel output for
predicting starting salary (Y using number of hours spent studying per day (X) for a sample of 51 students. NOTE: Only partial output is shown.
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
0.8857
0.7845
0.7801
1.3704
Observations
51
ANOVA
df
SS
MS
F
Significance F
Regression
1
335.0472 335.0473
178.3859
Residual
1.8782
Total
50
427.0798
Standard
Upper 95%
-1.0865
Coefficients
Error
t Stat
P-value
Lower 95%
-1.8940
0.4018
-4.7134 2.051E-05
-2.7015
Intercept
Hours
0.9795
0.0733
13.3561
5.944E-18
0.8321
1.1269
Note: 2.051E-05 = 2.051 * 10-0.5 and 5.944E-18 = 5.944 * 10-18
Referring to Table 12-9, the 90% confidence interval for the average change in SALARY (in thousands of dollars) as a result of spending an extra hour per
day studying is
Seleccione una:
O A.
wider than [-2.70159, -1.08654].
O B.
narrower than [-2.70159, -1.08654].
wider than [0.8321927, 1.12697].
OD.
narrower than [0.8321927, 1.12697].
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