Advanced Business Research — DU B.Com (P) Previous Year Question Papers

A manufacturing company conducted a study to examine the effect of Machine Maintenance \t Cost, Employee Experience, and Production Time on Monthly Production Output. Pata were collected from 75 manufacturing units and analyzed using Multiple Regression Analysis. The following output was obtained.. Stud.y the following regression ouq?ut carefully and,answer the questions given below: Model Summary Statistic Value Adjusted R Square 0.652 `r, Standard Error 5.12'1?1'\ ANOVA Table , ` + Sum of Squares Mean Square Coefficients Table Unstandardized Coefficient ,(a) TIJf Machine Maintenance Cost Employee Experience (a) What is Multiple Regression Analysis? Expl,a.in :irLs`imj)ortance in busing;§s research. a) Write the estimated regression equation from the above output. (c) Interpret the following values: 2. Adjusted R Square (d) Interpret the coefficients of: 1. Machine Maintenance Cost 2. Production Time (e) Using the ANOVA table, state whether the regression model is statistically significant. (f) Which independent variable has the strongest effect on Monthly Production Output? Justify your answer using the regression coefficients. Factor (VIF) in regression analysis. (9) (b) Explain the meaning and significance of R-square and Adjusted R-square in regression analysis. (9) Producton (Machine Maintenan e Cost), → → Z B·$ {{9 Multiple Regression Model Summary ніF01Ф) (Statistic) HT7 (Value) Adjusted R Square 0.652 Standard Error 15.12 ANOVA Table Fila (Source). Sum of Squares df Mean Square F Sig. Coefficients Table TR (Variables) Unstandardized Coefficient (B) Std. Error t-value Sig. Machine Maintenance Cost 0.745 0.138 15.39 0.000 Employee Experience 1.120 (0.310 ' 3.61 10.001 Production Time -0.582 70.205 -284 10.006 () Multiple Regresion Analysis Research # R. Adjusted R Square 3. Machine Maintenance Cost P. Production Time (Statistically Significant) I (A dependent Variable HIN* 34 33YE (Monthly Prodúction Output) 4R (Regression An flan 9{4 (Adjusted R-square A hospital conducted a study to identify factors affecting whether a patient is likely to suffer from a lifestyle disease or not. The dependent variable was coded as: 1 = Disease Present 0 = Disease Absent The independent variables considered were: Daily Exercise Hours • Body Mass Index (BMI) Smoking Habit Score The following Logistic Regression output was obtained from the analysis of 150 patients. Study the following Logistic Regression output carefully and answer the questions given below: Model Summary Statistic Value -2 Log Likelihood 118.62 Cox & Snell R Square 0.438 Nagelkerke R Square 0.587 Classification Table Predicted Disease Variables in the Equation Daily Exercise Hours Smoking Habit Score (a) What is Logistic Regression? Explain its importance in research. a) Interpret the dependent variable coding used in this s.fudy. (c) Interpret the Nagelkerke R Square value obtained in the study. (d) hteapret the effect of: 1. Daily Exercise Hours