23.What is the sample covariance between Xand Y? A)136.313 B)155.786 C)181.750 D)159.032 24.What is the sample correlation coefficient between Xand Y? 25.What is the slope of the regression line of hours on income? 26.What is the y-intercept of the regression line of hours on income? 27.What is the regression sum of squares? A)691.062 B)1033.601 C)461.812 D)437.918 28.What is the error sum of squares?
48 29.What is the value of the coefficient of determination? 30.What is the estimate of the variance of the population model error? 31.What is the standard error of the slope of the regression line of hours on income? A)0.256 B)0.234 C)0.211 D)0.269 32.What is the value of the test statistic for testing0111:0 vs. :0HH? 33.An indication of no linear relationship between two variables would be a: THE NEXT EIGHT QUESTIONS ARE BASED ON THE FOLLOWING INFORMATION: A sales manager is interested in determining the relationship between the amount spent on advertising and total sales. The manager collects data for the past 24 months and runs a regression of sales on advertising expenditures. The results are presented below but, unfortunately, some values identified by asterisks are missing. SUMMARY OUTPUT Regression Statistics Multiple R 0.492 R Square 0.242 Adjusted R Square 0.208 Standard Error 40.975 Observations 24.000
49 ANOVA df SS MS F Significance F Regression 1 11809.406 11809.406 7.034 * Residual * * * Total * * Coefficients Standard Error t Stat P-value Intercept * 26.239 4.021 0.001 Advertising 2.015 * 2.652 0.015 34.What are the degrees of freedom for residuals? 35.What is the value of mean square error? A)1678.9 B)1,554.2 C)1,493.6 D)1,407.336.What are the total degrees of freedom? 37.What is the value of residual sum of squares? 38.What is the value of total sum of squares? 39.What is the value of significance F? A)Larger than 0.10 B)Smaller than 0.01 C)Smaller than 0.05 D)None of the above.40.What is the regression coefficient of y-intercept? 41.What is the standard error of estimate?
50 42.A regression analysis between sales (in $1000) and advertising (in $100) resulted in the following least squares line:yˆ= 75 + 5x. This implies that if advertising is $800, then the predicted amount of sales (in dollars) is:
- Fall '19