IEPr X & Exp X * 313 x Ill STA X hw x UF App X UF App X & hwe X COL X COU X| COU X * COU X C 8.4. X C Pro X C Ple: X + V X -> C a users.stat.ufl.edu/~winner/sta4211/ALSM_5Ed_Kutner.pdf A E Applied linear statistical models.djvu 372 / 1415 - 150% + a. Fit regression model (8.49) and state the estimated regression function. b. Test whether the interaction term can be dropped from the model; use a = .05. State the alternatives. decision rule, and conclusion. If the interaction term cannot be dropped from 369 the model. describe the nature of the interaction effect. 8.21. In a regression analysis of on-the-job head injuries of warehouse laborers caused by falling objects, Y is a measure of severity of the injury, X , is an index reflecting both the weight of the object and the distance it fell, and X, and X3 are indicator variables for nature of head protection worn at the time of the accident, coded as follows: Type of Protection X2 X3 Hard hat O 370 Bump cap oo - None O . The response function to be used in the study is E(Y) = Bo + BIX, + B2X2 + BxX3- a. Develop the response function for each type of protection category. b. For each of the following questions, specify the alternatives Ho and Ha for the appropriate test: (1) With X, fixed, does wearing a bump cap reduce the expected severity of injury as compared with wearing no protection? (2) With X, fixed, is the expected severity of injury the same when wearing a hard hat as when wearing a bump cap? 371 8.22. Refer to tool wear regression model (8.36). Suppose the indicator variables had been defined as follows: X2 = I if tool model M2 and 0 otherwise, X3 = 1 if tool mode! M3 and 0 otherwise, X. = 1 iftool model M4 and () otherwise. Indicate the meaning of each of the following: (1) P3, (2) By - B3. (3) BI. 372 Type here to search O Ei 8:33 PM 56.F Sunny ~ 4/11/2022