PLEASE SHOW ALL EXCEL FORMULAS USED FOR EACH CALCULATIONS. A STEP BY STEP WALKTHROUGH OF HOW TO DO THE PROBLEM. Thank you so much for your help! Hours Feet Elevator Elevator code 24.00 545 Yes 1 13.50 400 Yes 1 26.25 562 No 0 25.00 540 No 0 9.00 220 Yes 1 20.00 344 Yes 1 22.00 569 Yes 1 11.25 340 Yes 1 50.00 900 Yes 1 12.00 285 Yes 1 38.75 865 Yes 1 40.00 831 Yes 1 19.50 344 Yes 1 18.00 360 Yes 1 28.00 750 Yes 1 27.00 650 Yes 1 21.00 415 No 0 15.00 275 Yes 1 25.00 557 Yes 1 45.00 1028 Yes 1 29.00 793 Yes 1 21.00 523 Yes 1 22.00 564 Yes 1 16.50 312 Yes 1 37.00 757 No 0 32.00 600 No 0 34.00 796 Yes 1 25.00 577 Yes 1 31.00 500 Yes 1 24.00 695 Yes 1 40.00 1054 Yes 1 27.00 486 Yes 1 18.00 442 Yes 1 62.50 1249 No 0 53.75 995 Yes 1 79.50 1397 No 0 USE THE ABOVE NUMBER AND ANSWER (A.) ACCORDING TO THE CHART PARTS B AND BELOW ARE FROM THE EXAMPLE___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ The owner of a moving company wants an accurate method to predict the total number of labor hours that will be required to complete upcoming moves. The owner has decided to use the number of cubic feet moved and whether there is an elevator in the apartment building as the independent variables and has collected data for 36 moves. Use the data set given below to complete parts (a) through (g). For parts (a) through (d), do not include an interaction term EEB Click the icon to view the data table. a. State the multiple regression equation that predicts labor hours, when there is no elevator and define x2 to be 1 when there is an elevator. using the number of cubic fet moved, X1. and whether there is an elevator, X2. Define X2 to be o (Round to three decimal places as needed.) b. Interpret the regression coefficients in (a) To interpret the coefficient of a multiple regression model, hold constant all other coefficients in the model and then explain the effect of an increase of 1 in that coefficient. c. At the 0.05 level of significance, determine whether each independent variable makes a contribution to the regression model. Test the first independent variable, Feet. To test this variable, determine whether the slope, p1, of the variable Feet, Xj, is significantly different from 0. Determine the null and alternative hypotheses d. Construct and interpret a 95% confidence interval estimate of the population slope of the relationship between labor hours and number of cubic feet moved. Although either technology or the formula for confidence interval estimate of the slope could be used, for the purpose of this solution, use technology. Some technology will include the confidence interval estimates into the regression output, as shown below. Add the 95% confidence interval estimate for Feet into the output, rounding to three decimal places. Note that several values in the table have been left blank because they will be used in part (e). e. Construct and interpret a 95% confidence interval estimate of the population slope of the relationship between labor hours and whether there is an elevator. Use technology to add the 95% confidence interval estimate for Elevator into the output, rounding to three decimal places. Predictor Constant Feet Elevator Coef SE Coef 7.689 0.046 9.316 P 2.522 3.048 0.0045 0.003 14.437 0.0000 1.826 -5.101 0.0000 Lower 95% 2.557 0.039 13.032 Upper 95% 12.821 0.052 5.601 f. Add an interaction term to the model and, at the 0.05 level of significance, determine whether it makes a significant contribution to the model To find the multiple regression equation that predicts the selling price, based on the number of cubic feed moved, X1, whether there is an elevator, X2, and the interaction term, X3 -X1 XX2, first determine the values of the interaction term, X3 Multiply the values of the variable Feet by the corresponding values of the dummy variable Elevator (replacing "Yes" with 1 and "No" with 0) to determine the values of the interaction term. g. On the basis of the results of (c) and (f), which model is most appropriate? Explain. Review parts (c) and (f) carefully to determine which regression model is the most appropriate. Recall that the p-value for the test of H0: β1-0 versus H1 : β1 #0 (interaction term not included) was 0.0000, the p-value for the test of H0: β2-0 versus H1 : β2 #0 (interaction term not included) was 0.0000, and the p-value for the test of H0: ß3-0 versus H1 : ß3 #0 (interaction term included) was 0.0119. The owner of a moving company wants an accurate method to predict the total number of labor hours that will be required to complete upcoming moves. The owner has decided to use the number of cubic feet moved and whether there is an elevator in the apartment building as the independent variables and has collected data for 36 moves. Use the data set given below to complete parts (a) through (g). For parts (a) through (d), do not include an interaction term EEB Click the icon to view the data table. a. State the multiple regression equation that predicts labor hours, when there is no elevator and define x2 to be 1 when there is an elevator. using the number of cubic fet moved, X1. and whether there is an elevator, X2. Define X2 to be o (Round to three decimal places as needed.) b. Interpret the regression coefficients in (a) To interpret the coefficient of a multiple regression model, hold constant all other coefficients in the model and then explain the effect of an increase of 1 in that coefficient. c. At the 0.05 level of significance, determine whether each independent variable makes a contribution to the regression model. Test the first independent variable, Feet. To test this variable, determine whether the slope, p1, of the variable Feet, Xj, is significantly different from 0. Determine the null and alternative hypotheses d. Construct and interpret a 95% confidence interval estimate of the population slope of the relationship between labor hours and number of cubic feet moved. Although either technology or the formula for confidence interval estimate of the slope could be used, for the purpose of this solution, use technology. Some technology will include the confidence interval estimates into the regression output, as shown below. Add the 95% confidence interval estimate for Feet into the output, rounding to three decimal places. Note that several values in the table have been left blank because they will be used in part (e). e. Construct and interpret a 95% confidence interval estimate of the population slope of the relationship between labor hours and whether there is an elevator. Use technology to add the 95% confidence interval estimate for Elevator into the output, rounding to three decimal places. Predictor Constant Feet Elevator Coef SE Coef 7.689 0.046 9.316 P 2.522 3.048 0.0045 0.003 14.437 0.0000 1.826 -5.101 0.0000 Lower 95% 2.557 0.039 13.032 Upper 95% 12.821 0.052 5.601 f. Add an interaction term to the model and, at the 0.05 level of significance, determine whether it makes a significant contribution to the model To find the multiple regression equation that predicts the selling price, based on the number of cubic feed moved, X1, whether there is an elevator, X2, and the interaction term, X3 -X1 XX2, first determine the values of the interaction term, X3 Multiply the values of the variable Feet by the corresponding values of the dummy variable Elevator (replacing "Yes" with 1 and "No" with 0) to determine the values of the interaction term. g. On the basis of the results of (c) and (f), which model is most appropriate? Explain. Review parts (c) and (f) carefully to determine which regression model is the most appropriate. Recall that the p-value for the test of H0: β1-0 versus H1 : β1 #0 (interaction term not included) was 0.0000, the p-value for the test of H0: β2-0 versus H1 : β2 #0 (interaction term not included) was 0.0000, and the p-value for the test of H0: ß3-0 versus H1 : ß3 #0 (interaction term included) was 0.0119.