It is believed that the average number of hours spent studying per day (HOURS) during undergraduate...

 

 

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It is believed that the average number of hours spent studying per day (HOURS) during undergraduate cducation should have a positive linear relationship with starting salary (SALARY. measured in thousands of dollars per month) after graduation. Given below is the Excel output from regressing starting salary on number of hours spent studying per day for a sample of 51 students. NOTE Some of the numbers in the output are purposely erased. Regression Statistics Multipie R RSquare Adjusted R Square Standard Error Observations 0.8857 0.7845 0.7801 1.3704 51 ANOVA df 335.0472 SS MS Sigmicance E Regression Residua! 335.0473 178 3859 18782 Total 50 427.0798 Coefficients Intercept -1.8940 0,9795 Standard Error 0.4018 t Stat -4.7134 p-talue 2.0516-05 5.944E-18 Lower 95% Upper 99% 2.7015 0.8321 -10865 Hours 0.0733 13.3561 1.1269 17) Referring to Table 13-9, to test the claim that average SALARY depends on HOURS, against the null hypothesis that average SALARY does not depend linearly on HOURS, the p-value of the test statistic is A) 2.051E-05 B) (5,944E-18)/2 C) (2.051E-05)/2 D) 5.944E-18 18) Referring to Table 13-9, the error sum of squares (SSE) of the above regression is A) 92.03 B) 427.07 C) 335.047 D) 1.878 19) Referring to Table 13-9, the value of the measured t test statistic to test whether average SALARY depends linearly on HOURS is A) -4.7134 B) 0.9795 C) 13.3561 D) -1.8940 20) Referring to Table 13-9, the estimated average change in salary (in thousands of dollars) as a result of spending an extra hour per day studying Le, slope is A) 0.7845 B) 335.0473 C) 0.9795 D) -1.894 B) (5.944E-18)/2 2.051E-0 C) (2.051E-05)2 D) 5.944E-18 18) Referring to Table 13-9, the error sum of squares (SSE) of the above regression is A) 92.03 B) 427.07 C) 335.047 D) 1.878 19) Referring to Table 13-9, the value of the measured t test statistic to test whether average SALARY depends linearly on HOURS is A) -4.7134 B) 0.9795 C) 13.3561 D) -1.8440 20) Referring to Table 13-9, the estimated average change in salary (in thousands of dollars) as a result of spending an extra hour per day studying, i.e, slope is A) 0.7845 B) 335.0473 C) 0.9795 D) -1.894 21) Referring to Table 13-9, the degrees of freedom for testing whether HOURS affect SALARY in using F are A) 50, 1 B) 1, 50 C) 1, 49 D) 49, 1 22) Referring to Table 13-9, the correlation coefficient between Hours and Salary is A) 0.8857 C) 0.7845 B) - 0.8857. D) - 0.7845. 23) To test Ho : B1 -0 vs. H1 : B1 =0 for a regression, we can use both t and p-Value A) True B) False 24) In question 23) rejecting Ho means x is a cause of Y. A) The above statement is always B) The above C) The above statement is not true always statement is true true some times 25) The coefficient of determination (R) represents the ratio of SSR to SST. A) True, B) False It is believed that the average number of hours spent studying per day (HOURS) during undergraduate cducation should have a positive linear relationship with starting salary (SALARY. measured in thousands of dollars per month) after graduation. Given below is the Excel output from regressing starting salary on number of hours spent studying per day for a sample of 51 students. NOTE Some of the numbers in the output are purposely erased. Regression Statistics Multipie R RSquare Adjusted R Square Standard Error Observations 0.8857 0.7845 0.7801 1.3704 51 ANOVA df 335.0472 SS MS Sigmicance E Regression Residua! 335.0473 178 3859 18782 Total 50 427.0798 Coefficients Intercept -1.8940 0,9795 Standard Error 0.4018 t Stat -4.7134 p-talue 2.0516-05 5.944E-18 Lower 95% Upper 99% 2.7015 0.8321 -10865 Hours 0.0733 13.3561 1.1269 17) Referring to Table 13-9, to test the claim that average SALARY depends on HOURS, against the null hypothesis that average SALARY does not depend linearly on HOURS, the p-value of the test statistic is A) 2.051E-05 B) (5,944E-18)/2 C) (2.051E-05)/2 D) 5.944E-18 18) Referring to Table 13-9, the error sum of squares (SSE) of the above regression is A) 92.03 B) 427.07 C) 335.047 D) 1.878 19) Referring to Table 13-9, the value of the measured t test statistic to test whether average SALARY depends linearly on HOURS is A) -4.7134 B) 0.9795 C) 13.3561 D) -1.8940 20) Referring to Table 13-9, the estimated average change in salary (in thousands of dollars) as a result of spending an extra hour per day studying Le, slope is A) 0.7845 B) 335.0473 C) 0.9795 D) -1.894 B) (5.944E-18)/2 2.051E-0 C) (2.051E-05)2 D) 5.944E-18 18) Referring to Table 13-9, the error sum of squares (SSE) of the above regression is A) 92.03 B) 427.07 C) 335.047 D) 1.878 19) Referring to Table 13-9, the value of the measured t test statistic to test whether average SALARY depends linearly on HOURS is A) -4.7134 B) 0.9795 C) 13.3561 D) -1.8440 20) Referring to Table 13-9, the estimated average change in salary (in thousands of dollars) as a result of spending an extra hour per day studying, i.e, slope is A) 0.7845 B) 335.0473 C) 0.9795 D) -1.894 21) Referring to Table 13-9, the degrees of freedom for testing whether HOURS affect SALARY in using F are A) 50, 1 B) 1, 50 C) 1, 49 D) 49, 1 22) Referring to Table 13-9, the correlation coefficient between Hours and Salary is A) 0.8857 C) 0.7845 B) - 0.8857. D) - 0.7845. 23) To test Ho : B1 -0 vs. H1 : B1 =0 for a regression, we can use both t and p-Value A) True B) False 24) In question 23) rejecting Ho means x is a cause of Y. A) The above statement is always B) The above C) The above statement is not true always statement is true true some times 25) The coefficient of determination (R) represents the ratio of SSR to SST. A) True, B) False

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18 From the table The pvalue of hours 59447 182 19 errors s... View the full answer