3. The ABX Company is interested in conducting a study to identify factors that affect absenteeism...

  

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3. The ABX Company is interested in conducting a study to identify factors that affect absenteeism among its production employees. After reviewing the literatures on absenteeism and interviewing several production supervisors and a number of employees, the researcher in charge of the project defined the variables shown as below ABSENT =the number of distinct occasions that the worker was absent during the year COMPLX = a measure of job complexity, ranging from 0 to 100 PAY = basic hourly pay rate in dollars SENIOR = number of complete years with the company AGE - age of the employee DEPEND employee response to the question "how many individuals other than yourself depend on you for most of their financial support? A sample of 77 employees was randomly selected, the data was contained in the file named "PS2A.xlsx" (sheet = "PS2A.Q3"). Consider a multiple regression model with absenteeism as the response and all other variables as the covariates. The R output is given below with some missing figures. Estimate Std. Error t value Pr(>|t|) 3.626609 1.387114 2.615 (Intercept) 0.0109 COMPLX PAY SENIOR AGE -0.015955 0.006883 -2.318 0.044839 0.166481 -0.038100 0.048240 -0.790 -0.025584 0.032503 -0.787 0.4338 0.0233 0.4323 DEPEND -0.040499 0.123385 -0.328 0.7437 Residual standard error: 1.38 on _ degrees of freedom Multiple R-squared: 0.1874, Adjusted R-squared: F-statistic: 3.276 on and DF, p-value: (a) Write down the fitted regression model. (b) Interpret the meaning of the estimated slope coefficient for COMPLX. (c) What is the residual degree of freedom. (d) Compute the adjusted R. (e) Compute the SSR of the model. (f) Test the slope coefficient of PAY. Use 5% level of significance. ABSENT COMPLX PAY SENIOR AGE DEPEND 2 0 45 6 3 28 2 3. 1 76 8 10 42 1 0 56 5 9 40 5 2 76 8 7 34 2 5 0 70 8 14 39 2 7 1 69 6 9 44 0 B 1 56 6 3 40 1 9 1 56 5 1 35 0 0 2 43 7 9 32 0 1 1 76 6 1 41 1 3 30 5 1 27 1 3 2 50 7 9 40 0 4 1 10 5 1 30 1 5 3 69 6 4 35 0 6 2 67 8 3 33 1 7 0 69 6 4 32 1 8 4 70 7 8 37 1 9 7 13 6 1 26 2 20 3 16 8 3 36 2 21 2 52 7 5 28 2 22 2 52 7 16 40 1 13 4 3 6 2 26 0 24 2 6 5 4 38 2 25 0 67 8 6 33 1 26 3 10 5 1 26 0 27 3 89 10 18 48 0 28 3 21 5 2 34 1 9 0 34 6 4 26 1 0 2 12 8 6 40 2 1 3 70 7 2 34 2 2 1 69 6 11 49 2 3 4 13 6 1 35 1 4 2 30 6 13 51 5 55 1 43 5 1 25 1 6 3 8 6. 2 29 0 7 2 69 7 3 34 2 8 4 30 5 T 36 2 9 4 23 5 1 31 2 0 4 16 7 1 28 2 1 3 11 6 1 32 2 2 2 16 6 1 30 2 3 6 50 6 2 30 2 4 3 50 6 2 28 0 5 1 69 9 4 31 4 -6 2 10 6 2 34 1 7 1 43 7 26 54 1 8 1 12 6 1 28 0 -9 3 76 7 5 28 2 0 2 56 5 2 35 2 1 0 6 6 8 43 4. 0 8 7 3 29 4 3 1 45 5 2 32 3 4 3 43 6 5 31 3. 55 6 23 5 1 26 1 6 3 1 7 7 46 1 7 2 82 8 1 23 1 8 2 1 6 1 20 0 9 4 1 6 1 35 3 0 3 70 8 4 32 2 51 0 76 7 6 34 0 2 0 82 9 7 38 0 3 1 50 6 9 33 2 4 1 70 9 8 45 1 5 1 81 7 5 27 1 6 2 70 8 9 33 4 57 3 1 7 2 30 3 58 2 8 6 1 32 2 9 2 23 5 2 24 3 20 2 21 6 12 47 4 2 82 9 7 33 5 1 67 9 28 54 3 13 0 81 7 18 45 2 "4 1 43 8 6 40 0 5 4 6 6 3 21 1 "6 3 13 7 8 29 4 7 2 52 7 7 31 1 8 3 52 5 1 27 1 3. The ABX Company is interested in conducting a study to identify factors that affect absenteeism among its production employees. After reviewing the literatures on absenteeism and interviewing several production supervisors and a number of employees, the researcher in charge of the project defined the variables shown as below ABSENT =the number of distinct occasions that the worker was absent during the year COMPLX = a measure of job complexity, ranging from 0 to 100 PAY = basic hourly pay rate in dollars SENIOR = number of complete years with the company AGE - age of the employee DEPEND employee response to the question "how many individuals other than yourself depend on you for most of their financial support? A sample of 77 employees was randomly selected, the data was contained in the file named "PS2A.xlsx" (sheet = "PS2A.Q3"). Consider a multiple regression model with absenteeism as the response and all other variables as the covariates. The R output is given below with some missing figures. Estimate Std. Error t value Pr(>|t|) 3.626609 1.387114 2.615 (Intercept) 0.0109 COMPLX PAY SENIOR AGE -0.015955 0.006883 -2.318 0.044839 0.166481 -0.038100 0.048240 -0.790 -0.025584 0.032503 -0.787 0.4338 0.0233 0.4323 DEPEND -0.040499 0.123385 -0.328 0.7437 Residual standard error: 1.38 on _ degrees of freedom Multiple R-squared: 0.1874, Adjusted R-squared: F-statistic: 3.276 on and DF, p-value: (a) Write down the fitted regression model. (b) Interpret the meaning of the estimated slope coefficient for COMPLX. (c) What is the residual degree of freedom. (d) Compute the adjusted R. (e) Compute the SSR of the model. (f) Test the slope coefficient of PAY. Use 5% level of significance. ABSENT COMPLX PAY SENIOR AGE DEPEND 2 0 45 6 3 28 2 3. 1 76 8 10 42 1 0 56 5 9 40 5 2 76 8 7 34 2 5 0 70 8 14 39 2 7 1 69 6 9 44 0 B 1 56 6 3 40 1 9 1 56 5 1 35 0 0 2 43 7 9 32 0 1 1 76 6 1 41 1 3 30 5 1 27 1 3 2 50 7 9 40 0 4 1 10 5 1 30 1 5 3 69 6 4 35 0 6 2 67 8 3 33 1 7 0 69 6 4 32 1 8 4 70 7 8 37 1 9 7 13 6 1 26 2 20 3 16 8 3 36 2 21 2 52 7 5 28 2 22 2 52 7 16 40 1 13 4 3 6 2 26 0 24 2 6 5 4 38 2 25 0 67 8 6 33 1 26 3 10 5 1 26 0 27 3 89 10 18 48 0 28 3 21 5 2 34 1 9 0 34 6 4 26 1 0 2 12 8 6 40 2 1 3 70 7 2 34 2 2 1 69 6 11 49 2 3 4 13 6 1 35 1 4 2 30 6 13 51 5 55 1 43 5 1 25 1 6 3 8 6. 2 29 0 7 2 69 7 3 34 2 8 4 30 5 T 36 2 9 4 23 5 1 31 2 0 4 16 7 1 28 2 1 3 11 6 1 32 2 2 2 16 6 1 30 2 3 6 50 6 2 30 2 4 3 50 6 2 28 0 5 1 69 9 4 31 4 -6 2 10 6 2 34 1 7 1 43 7 26 54 1 8 1 12 6 1 28 0 -9 3 76 7 5 28 2 0 2 56 5 2 35 2 1 0 6 6 8 43 4. 0 8 7 3 29 4 3 1 45 5 2 32 3 4 3 43 6 5 31 3. 55 6 23 5 1 26 1 6 3 1 7 7 46 1 7 2 82 8 1 23 1 8 2 1 6 1 20 0 9 4 1 6 1 35 3 0 3 70 8 4 32 2 51 0 76 7 6 34 0 2 0 82 9 7 38 0 3 1 50 6 9 33 2 4 1 70 9 8 45 1 5 1 81 7 5 27 1 6 2 70 8 9 33 4 57 3 1 7 2 30 3 58 2 8 6 1 32 2 9 2 23 5 2 24 3 20 2 21 6 12 47 4 2 82 9 7 33 5 1 67 9 28 54 3 13 0 81 7 18 45 2 "4 1 43 8 6 40 0 5 4 6 6 3 21 1 "6 3 13 7 8 29 4 7 2 52 7 7 31 1 8 3 52 5 1 27 1

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