Y=net profit earned from the client X1=number of hours spent working with the client X2= type of client (1 manufacturing, 2 service, 3 governmental) Y2343410027812081402600-750345124741973201X14753255526253147452632X212322331123

In an effort to explain variation in client profitability, an accounting firm collected the data shown in the accompanying table. The firm wants to know if it needs the client type in addition to the number of hours spent working with the client to predict the net profit earned from the client a. Fit a model to the data that incorporates the number of hours spent working with the client and the type of client as independent variables. (Hint: Client type has three levels.) b. Fit a second-order model to the data, using the same dummy variables for client type. Does this model provide a better fit than that found in part a? Which model would you recommend be used? Click the icon to view the data. a. Since client type has three levels, use two dummy variables for client type, *2 = 1 if manufacturing, 0 otherwise and *3 = 1 if service, 0 otherwise. Complete the regression model below. (Round the constant and the x2 and x3 coefficients to the nearest integer as needed. Round the x, coefficient to one decimal place as needed.) b. Complete the regression model below. (Round the constant and the x2 and x3 coefficients to the nearest integer as needed, Round the x, coefficient to one decimal place as needed. Round the x, coefficient to two decimal places as needed.) The adjusted R-squared value for the first-order model is |and the adjusted R-squared value for the second-order model is , so model provides a better fit. (Round to three decimal places as needed.) For a = 0.05, significant in the first-order model. In the second-order model, significant. So, model is effective at explaining variation in the dependent variable, client profitability Therefore, recommend model