Log_Profit Log_Accounts Log_Commission 9.18822 2.986607 6.85376 9.905282 2.932899 6.907074 10.951461 5.4337 8.569829 9.260299 2.719593 5.979287 9.804248 2.397203 5.707553 10.449925 2.834732 5.150056 10.323287 3.294323 8.239854 9.880874 2.763927 7.804443 11.612133 4.104468 7.82424 11.593686 4.363125 7.09399 8.135523 1.954857 4.15 9.641111 2.191404 7.074719 9.618778 3.040843 5.652898 9.416765 2.634329 7.741934 9.428018 2.306894 7.758824 9.621387 2.479573 3.52545 9.984954 2.208577 4.623314 10.280648 4.497216 7.230232 8.988956 1.949576 5.299601 9.218622 3.34773 6.585078 9.149911 2.473691 6.02813 9.272206 2.56783 6.218747 8.721558 1.95791 2.850786 9.733584 2.558275 7.258979 9.68721 2.957688 6.327647 11.021886 4.614978 9.414388 9.993674 5.567472 6.254576 9.690523 2.761974 5.541922 9.323856 2.082698 7.258639 10.429696 3.098112 5.490311 9.634762 2.78665 6.500225 9.874933 2.297651 8.07524 9.639201 3.105484 7.853177 11.044109 3.731807 7.85289 9.976143 4.387096 6.53722 10.313801 3.167581 6.188317 11.106849 3.10454 9.296623 10.778914 6.167602 8.114565 10.829336 4.825065 6.238091 11.174661 3.828103 7.754126 10.666863 4.545605 8.035586 8.723863 1.609912 8.351724 9.002575 2.848514 6.398443 8.625349 2.489578 7.297691 9.047002 2.078209 6.304028 8.780348 2.294562 5.71998 9.119934 4.228053 7.028475 8.910356 2.718624 5.173921 8.527816 1.096253 5.322508 9.043018 2.630362 6.720518

An analyst for a firm has been asked to determine what factors can be used to predict profits. Use the following information and data table provided to complete parts (a) through (1) below. Click the icon to view the information. B Click the icon to view the data table. ... (a) Examine scatterplots of the response versus the two explanatory variables as well as the scatterplot between the explanatory variables. Be sure to keep all of the variables on the scale of natural logs. Do you notice any unusual features in the data? Do the relevant plots appear straight enough for multiple regression? Graph Log Profit versus Log Accounts. Choose the correct graph below. OA OB. . D. Alog Profit ALog Profit A Log Profit 14-4 14 Q Alog Profit Q 14- 14 its .hk . . 04 6 12 0 10 10 Log Accounts Log Accounts Log Accounts Log Accounts Graph Log Profit versus Log Commission. Choose the correct graph below. B. D. . ALog Profit 142 Q A Log Profit 14+ OC. A Log Profit 14- Q ALog Profit 14- Q 19 *** * 0 6 0 10 12 12 O 10 Log Commission Log Commission Log Commission Log Commission B. Graph Log Accounts versus Log Commission. Choose the correct graph below. OA. Log Accounts 14- B. A Log Accounts 14- OC. Alog Accounts 14- D. A Log Accounts 14- o o Q ER 6- 0 10 8 0 0 10 Log Commission Log Commission Log Commission Log Commission Do you notice any unusual features in the data? O A. Yes, there is an outlier that will need to be removed before the regression can be completed. B. Yes, there is evidence of unequal variance in one of the plots. OC. Yes, there appears to be a nonlinear trend in one of the plots. O D. No, there are no unusual features in the data. Do the relevant plots appear straight enough for multiple regression? O A. No, because there is significant curvature in two of the plots. B. Yes, because there is no significant curvature in any of the plots. C. No, because there is significant curvature in all of the plots. D. No, because there is significant curvature in one of the plots. (b) Fit the multiple regression that expands the one-predictor equation by adding the second explanatory variable to the model. Summarize the estimates obtained for the fitted model. (Round R and se to three decimal places. Round all coefficients and standard errors to three decimal places. Round all test statistics to two decimal places. Round all probabilities to three decimal places.) R? se Term Estimate Std Error t-statistic p-value Intercept Log Accounts Log Commission (c) Does the fit of this model meet the conditions of the MRM? O A. Yes, because the plots of the residuals look random, the plots of the residuals show no evidence of unequal variance, and the normal plot shows that the residuals are nearly normal. OB. No, because the plots of the residuals show no evidence of unequal variance, and the normal plot shows that the residuals are nearly normal, but the plots of the residuals show a pattern. OC. No, because the plots of the residuals look random, and the normal plot shows that the residuals are nearly normal, but the plots of the residuals show evidence of unequal variance OD. No, because the plots of the residuals look random, and the plots of the residuals show no evidence of unequal variance, but the normal plot does not show that the residuals are nearly normal. (d) Does the confidence interval for the partial elasticity for the number of accounts indicate a large shift from the marginal elasticity? What is the confidence interval for the partial elasticity for the number of accounts? OD (Round to three decimal places as needed.) Does the confidence interval for the partial elasticity for the number of accounts indicate a large shift from the marginal elasticity? A. Yes. The marginal elasticity falls within the confidence interval for the partial elasticity, so there is a large shift from the marginal elasticity. B. Yes. The marginal elasticity falls outside the confidence interval for the partial elasticity, so there is not much of a shift from the marginal elasticity. C. No. The marginal elasticity falls outside the confidence interval for the partial elasticity, so there is a large shift from the marginal elasticity. D. No. The marginal elasticity falls within the confidence interval for the partial elasticity, so there is not much of a shift from the marginal elasticity. (e) Use a path diagram to illustrate why the marginal elasticity and partial elasticity are either similar or different A. B. Accounts Profit Accounts Profit Commission Commission Oc. D. Accounts Profit Accounts Profit Commission Commission (1) Which would likely be more successful in raising the performance of new hires: a training program that increased the number of accounts by 5% but did not change early selling, or a program that raised both by 2.5%? Can you answer this question from the estimated model? O A. A program that increased both by 2.5% would be the most successful. This is based on the relationship between the explanatory variables and not from the model. OB. A program that increased the number of accounts by 5% but did not change early selling would be the most successful. This can be determined from the model, which shows that the number of accounts increases the profit more than early selling does. OC. A program that increased both by 2.5% would be the most successful. This can be determined from the model, which shows that both the number of accounts and early selling increases the profit by a similar amount. OD. A program that increased early selling by 5% but did not change the number of accounts would be the most successful. This can be determined from the model, which shows that early selling increases the profit more than the number of accounts does. An analyst for a firm has been asked to determine what factors can be used to predict profits. Use the following information and data table provided to complete parts (a) through (1) below. Click the icon to view the information. B Click the icon to view the data table. ... (a) Examine scatterplots of the response versus the two explanatory variables as well as the scatterplot between the explanatory variables. Be sure to keep all of the variables on the scale of natural logs. Do you notice any unusual features in the data? Do the relevant plots appear straight enough for multiple regression? Graph Log Profit versus Log Accounts. Choose the correct graph below. OA OB. . D. Alog Profit ALog Profit A Log Profit 14-4 14 Q Alog Profit Q 14- 14 its .hk . . 04 6 12 0 10 10 Log Accounts Log Accounts Log Accounts Log Accounts Graph Log Profit versus Log Commission. Choose the correct graph below. B. D. . ALog Profit 142 Q A Log Profit 14+ OC. A Log Profit 14- Q ALog Profit 14- Q 19 *** * 0 6 0 10 12 12 O 10 Log Commission Log Commission Log Commission Log Commission B. Graph Log Accounts versus Log Commission. Choose the correct graph below. OA. Log Accounts 14- B. A Log Accounts 14- OC. Alog Accounts 14- D. A Log Accounts 14- o o Q ER 6- 0 10 8 0 0 10 Log Commission Log Commission Log Commission Log Commission Do you notice any unusual features in the data? O A. Yes, there is an outlier that will need to be removed before the regression can be completed. B. Yes, there is evidence of unequal variance in one of the plots. OC. Yes, there appears to be a nonlinear trend in one of the plots. O D. No, there are no unusual features in the data. Do the relevant plots appear straight enough for multiple regression? O A. No, because there is significant curvature in two of the plots. B. Yes, because there is no significant curvature in any of the plots. C. No, because there is significant curvature in all of the plots. D. No, because there is significant curvature in one of the plots. (b) Fit the multiple regression that expands the one-predictor equation by adding the second explanatory variable to the model. Summarize the estimates obtained for the fitted model. (Round R and se to three decimal places. Round all coefficients and standard errors to three decimal places. Round all test statistics to two decimal places. Round all probabilities to three decimal places.) R? se Term Estimate Std Error t-statistic p-value Intercept Log Accounts Log Commission (c) Does the fit of this model meet the conditions of the MRM? O A. Yes, because the plots of the residuals look random, the plots of the residuals show no evidence of unequal variance, and the normal plot shows that the residuals are nearly normal. OB. No, because the plots of the residuals show no evidence of unequal variance, and the normal plot shows that the residuals are nearly normal, but the plots of the residuals show a pattern. OC. No, because the plots of the residuals look random, and the normal plot shows that the residuals are nearly normal, but the plots of the residuals show evidence of unequal variance OD. No, because the plots of the residuals look random, and the plots of the residuals show no evidence of unequal variance, but the normal plot does not show that the residuals are nearly normal. (d) Does the confidence interval for the partial elasticity for the number of accounts indicate a large shift from the marginal elasticity? What is the confidence interval for the partial elasticity for the number of accounts? OD (Round to three decimal places as needed.) Does the confidence interval for the partial elasticity for the number of accounts indicate a large shift from the marginal elasticity? A. Yes. The marginal elasticity falls within the confidence interval for the partial elasticity, so there is a large shift from the marginal elasticity. B. Yes. The marginal elasticity falls outside the confidence interval for the partial elasticity, so there is not much of a shift from the marginal elasticity. C. No. The marginal elasticity falls outside the confidence interval for the partial elasticity, so there is a large shift from the marginal elasticity. D. No. The marginal elasticity falls within the confidence interval for the partial elasticity, so there is not much of a shift from the marginal elasticity. (e) Use a path diagram to illustrate why the marginal elasticity and partial elasticity are either similar or different A. B. Accounts Profit Accounts Profit Commission Commission Oc. D. Accounts Profit Accounts Profit Commission Commission (1) Which would likely be more successful in raising the performance of new hires: a training program that increased the number of accounts by 5% but did not change early selling, or a program that raised both by 2.5%? Can you answer this question from the estimated model? O A. A program that increased both by 2.5% would be the most successful. This is based on the relationship between the explanatory variables and not from the model. OB. A program that increased the number of accounts by 5% but did not change early selling would be the most successful. This can be determined from the model, which shows that the number of accounts increases the profit more than early selling does. OC. A program that increased both by 2.5% would be the most successful. This can be determined from the model, which shows that both the number of accounts and early selling increases the profit by a similar amount. OD. A program that increased early selling by 5% but did not change the number of accounts would be the most successful. This can be determined from the model, which shows that early selling increases the profit more than the number of accounts does