# Data Analysis for Decision Modeling Syllabus - MBA (PU)

• Short Name N/A
• Course code STT 502
• Semester Second Trimester
• Full Marks 100
• Pass Marks 60
• Credit Hrs 2
• Elective/Compulsary Compulsary

### Data Analysis for Decision Modeling

Chapter wise complete Notes.

### Course Description

Course Outline

Data Analysis for Decision Modeling consists of topics like Correlation, Regression and Time Series Analysis. After studying these topics students would able to know and work out relationship between variables related to business and can forecast prospective of business world. Linear Programming, Integer Programming and Network Analysis will help them to choose the best alternative in order to maximize total profit and minimize total cost in different business situations.

Course Objective

This course aims to acquaint with major statistical and quantitative tools used in modeling and analysis of business decision involving alternative choices.

### Unit Contents

Session Wise Teaching Plan

 Session Theme Reference 1. Correlation Analysis 1-2 Definition, Methods(Graphical and mathematical method), interpretation and Hypothesis testing of correlation coefficient TB1(CH12),TB2(CH12),RB1(CH11) and RB2 (CH14) 2. Regression Analysis(Simple) 3-6 Definition, Estimating equation, calculation and interpretation of y intercept and slope, Residual, Standard Error of Estimate and its interpretation Confidence Interval and Prediction Interval of estimating equation, Total Variation, explained variation, unexplained variation, coefficient of determination, correlation coefficient, Standard error of regression coefficient, Confidence interval and hypothesis testing of regression coefficient TB1(CH12),TB2(CH12),RB1(CH11) and RB2 (CH14) 3. Multiple Regression Analysis 7-11 Definition and Reasons for using multiple regression equation, Estimating multiple regression equation (2 independent variables), Confidence Interval and Prediction Interval of estimating equation and regression coefficient, Curvilinear regression equation, Residual Analysis and Autocorrelation, Durbin Watson Statistic, Dummy variable, Multicollinearity, Step wise regression and Analysis of SPSS output TB1(CH13),TB2(CH13),RB1(CH12) and RB2 (CH15) 4. Time Series Analysis 12-15 Components of time series analysis( Trend, Cyclical, Seasonal, Irregular), Trend analysis , moving average method, least square method, Error, TAD, MAD, MAPE, MSE, Cyclical Variation, Business cycle, Percent of trend, Relative cyclical residual, Seasonal Variation, Calculation of seasonal indices, Deseasonalization and Exponential Smoothing TB1(CH16),TB2(CH15),RB1(CH14) and RB2 (CH18) 5. Linear Programming 16-22 Introduction, Decision variable, objective function, constraints, Model formulation for Linear Programming  including Short Term Financing, Graphical solution , Determination of OV, active constraints, inactive constraints, slack, surplus, Alternative optimum solution for Linear Programming Problem, Sensitivity Analysis, Primal, Dual and Analysis of LINDO output TB3(CH2,CH3,CH4 & CH5), RB3(CH2&CH4),RB4(CH2 & CH4) and RB5 (CH8) 6. Integer Programming 23 Model formulation TB3(CH8),RB3(CH9), RB4(CH7) and RB5 (CH13) 7. Network Analysis 24-26 Activities, events, network diagram, Critical path, Critical activities, CPM, PERT, Crashing and model formulation RB3(CH6),RB4(CH12) and RB5 (CH12)

### Text and Reference Books

Books

 TB1 i. Business Statistics A First Course (Fifth Edition): David M. Levine, Timothy C. Krehbiel,  Mark L. Berenson and P.k Viswanathan TB2 ii. Statistics for Management: Richard I. Levin and David S. Rubin TB3 iii. Introductory Management Science: G.D Eppen, F.J Gould and C.P Schmidt RB1 iv. Practical Business Statistics: Andrew F. Siegel RB2 v. Statistics for Business and Economics (India Edition): David R. Anderson, Dennis J. Sweeney and Thomas A. Williams RB3 vi. Operation Research: Hamady A. Taha RB4 vii. Quantitative Techniques in Management: N.D Vohra RB5 viii. Quantitative Approaches to Management: Richard I. Levin, David S. Rubin, Joel P. Stinson and Everette

Activities

1. SPSS Training:

Students will be taken to lab during the trimester to show how they can use SPSS program in         order to analyze raw data.

1. Assignments:

Students will be given assignments after completion of each chapter.

1. Project:                                                                                                                                               Students will be given a project after completion of multiple regression analysis. They have to         collect data and analyze it through multiple regressions using SPSS.
2. Case Analysis:

Students will be given a case for analysis after completion of multiple regression analysis.

1. Article Review:

Students will be given an article for review after completion of simple regression analysis.

Evaluation System

Following is the breakup of the internal assessment for this course. As per the rule of Pokhara University, internal assessment has been given 60 percent weight in the final grading. The weight is distributed as follows:

 Evaluation Method Weight Marks 1. Class Performance, Behavior and Attendance 10% 6 2.  Case Analysis 10% 6 3. Assignments 10% 6 4. Project Work 15% 9 5. Article Review 10% 6 6. Mid-Term Examination 35% 21 7. Quiz 10% 6 Total 100% 60

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