A rm that operates a large sales force would like to build a system to monitor the progress of new agents. A key task for agents is to open new accounts. To build the system, the rm has monitored activities of new agents over the past two years. The response of interest is the profit to the rm (in dollars} of contracts sold by agents over their rst year. Among the possible explanations of this performance is the number of new accounts developed by the agent during the rst three months of work. Formulate the Simple Regression Model with Y given by the natural log of prot from sales and X given by the natural log of number of accounts, and complete parts (a) through (d). a Click the icon to view the data table. (I) b (a) Locate the most negative residual in the data. Which case is this? The value of the most negative residual is D. (Round to three decimal places as needed.) It occurs where the natural log ofthe number of accounts is X = D and the natural log of prot from sales is Y=|:|. (Type integers or decimals.) (b) Explain some characteristics that distinguish this employee from the others. (Hint: Consider the data in the Early Commission and Early Selling columns. Both are measured in dollars and measure the quality of business developed in the rst three months of working for this rm.} Choose the correct answer below. 0 A. This employee developed the greatest prot to O B. Unlike any of the other employees, this the rm over the rst year. employee has values of zero for early commission and early selling. 0 C. This employee developed the highest quality of O D. Unlike any of the other employees, this business during his rst three months. employee had a value of zero for the number of accounts. {c} How does the t change if this point is set aside, excluded from the original regression? Compare the tted model both with and without this employee. The tted model with this employee is ?i = D + |:|Xi. (Round to three decimal places as needed.) The tted model without this employee is = D + Dxi. (Round to three decimal places as needed.) The outlier (using the standard errors from the regression without the outlier to compare the estimates) causes the estimate of the intercept, Do, to shift El by D standard errors and the estimate of the slope, D1, to shift : by D standard errors. Further, the value of r2 1' and the value of 5.3 : (Round to one decimal place as needed.) {d} Explain the magnitude of the change in the t. Why does the t change by so much or so little? Choose the correct answer below. O A. The estimates of the intercept and slope change by a moderately small amount (less than 1.5 standard errors} partly because the outlier is strongly leveraged. O B. The estimates of the intercept and slope change by a moderately large amount (more than 1.5 standard errors} partly because the outlier is not leveraged. O C. The estimates of the intercept and slope change by a moderately large amount (more than 1.5 standard errors} partly because the outlier is strongly leveraged. O D. The estimates of the intercept and slope change by a moderately small amount (less than 1.5 standard errors} partly because the outlier is not leveraged