NUMBER	EXPER	TIME
1	9	66
1	11	74
3	11	88
4	8	99
6	9	134
6	9	120
7	10	178
8	9	139
9	8	187
11	10	227
11	10	225
12	7	270
13	9	265
14	9	301
15	10	343
16	11	383
17	10	383
20	9	515
19	9	474
20	9	495
22	9	628
22	9	636
23	10	660
24	10	731
25	11	752
26	8	800
27	10	863
28	9	918
29	9	976
30	10	1027

Transcribed Image Text:

Analyzing Customer Service: A computer repair service is examining the time taken on service calls to repair computers. Data were obtained for 30 service calls. The data are available in the worksheet entitled "COMPREP5". Information obtained includes: x = number of machines to be repaired (NUMBER) x2 = years of experience of service person (EXPER) y = time taken (in minutes) to provide service (TIME) Develop a polynomial regression model to predict average time on the service calls using EXPER and NUMBER as explanatory variables by fitting ALL the models below (Let NUMBERSQR and EXPERSQR represent squared variables.) Hint: Yes, this means that you have to fit ALL six of the models listed below separately. Create a table in EXCEL that organizes the standard error of the regression (Se) and Raj for each model to easily compare models based on these two indicators of model fit. (a) Choose the best fitting model. HINT: You will have to fit each one of these 6 models separately, organize the appropriate model fit statistics, and then, based on these results, make a decision concerning which model is the best fitting model. O TIME = -179 + 33 NUMBER+10.189 EXPER O TIME = 289 + 33 NUMBER - 92 EXPER +5.488 EXPERSQR O TIME = 286 + 16 EXPER O TIME = -84 + 33 NUMBER O TIME = 66 + 3.887 NUMBER + 0.943 NUMBERSQR + 0.371 EXPER O TIME = 69 + 3.865 NUMBER + 0.944 NUMBERSQR (b) Justify your model choice. (Use a 5% significance level.) O This model has significant p values for each coefficient, with a large standard error of the regression and a low R adj This model has the smallest standard error of the regression and the smallest Radj of all the models. This model has significant p values for each coefficient, with a small standard error of the regression and a high Rdj. This model has the largest standard error of the regression and the highest Rady of all the models. O This model has the largest standard error of the regression and the lowest Rdy of all the models. (c) What is the standard error of the regression for the best fitting model that you evaluated above? (Enter your answer to two decimal places.) Se= (d) What is Radj for the best fitting model that you evaluated above? (Enter your answer as a proportion and round to four decimal places.) Radj = Analyzing Customer Service: A computer repair service is examining the time taken on service calls to repair computers. Data were obtained for 30 service calls. The data are available in the worksheet entitled "COMPREP5". Information obtained includes: x = number of machines to be repaired (NUMBER) x2 = years of experience of service person (EXPER) y = time taken (in minutes) to provide service (TIME) Develop a polynomial regression model to predict average time on the service calls using EXPER and NUMBER as explanatory variables by fitting ALL the models below (Let NUMBERSQR and EXPERSQR represent squared variables.) Hint: Yes, this means that you have to fit ALL six of the models listed below separately. Create a table in EXCEL that organizes the standard error of the regression (Se) and Raj for each model to easily compare models based on these two indicators of model fit. (a) Choose the best fitting model. HINT: You will have to fit each one of these 6 models separately, organize the appropriate model fit statistics, and then, based on these results, make a decision concerning which model is the best fitting model. O TIME = -179 + 33 NUMBER+10.189 EXPER O TIME = 289 + 33 NUMBER - 92 EXPER +5.488 EXPERSQR O TIME = 286 + 16 EXPER O TIME = -84 + 33 NUMBER O TIME = 66 + 3.887 NUMBER + 0.943 NUMBERSQR + 0.371 EXPER O TIME = 69 + 3.865 NUMBER + 0.944 NUMBERSQR (b) Justify your model choice. (Use a 5% significance level.) O This model has significant p values for each coefficient, with a large standard error of the regression and a low R adj This model has the smallest standard error of the regression and the smallest Radj of all the models. This model has significant p values for each coefficient, with a small standard error of the regression and a high Rdj. This model has the largest standard error of the regression and the highest Rady of all the models. O This model has the largest standard error of the regression and the lowest Rdy of all the models. (c) What is the standard error of the regression for the best fitting model that you evaluated above? (Enter your answer to two decimal places.) Se= (d) What is Radj for the best fitting model that you evaluated above? (Enter your answer as a proportion and round to four decimal places.) Radj =