Data Analysis and Modeling Syllabus - BBA-TT (PU)

  • Short Name N/A
  • Course code SIT 201
  • Semester Fourth Semester
  • Full Marks 100
  • Pass Marks 45
  • Credit Hrs 3
  • Elective/Compulsary Compulsary

Data Analysis and Modeling

Chapter wise complete Notes.

Course Description

Course Objectives

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

Course Description

The component of the course includes regression analysis and models, time series analysis, and forecasting, linear programming models and applications, transportation and assignment models, network models, and simulation models with emphasis on business application.

Course Outcomes

By the end of this course students would be able to

  • calculate and interpret the meaning of correlation coefficient to measure the strength of relationship between two numerical variables,
  • calculate and interpret the meaning coefficient of determination to measure the predictive power of the simple as well as multiple regression,
  • forecast the future values using various models, and
  • optimize the resources in the business decision making process.
Unit Contents

Course Contents

Unit I Simple Correlation and Regression Models:

Measuring and Predicting Relationships                                                                  8 hours

Correlation: Meaning, Scatterplot, Karl Pearson correlation coefficient, Test of correlation coefficient.

Simple Linear Regression: Predicting of One Variable  from Another

Statistical model, Least square regression- assumptions, Standard error of estimate,      Coefficient of determination, Residual Analysis, Testing of regression coefficient.

Unit II Multiple Regression Models:

Predicting One Factor from Several Others                                                           8 hours

Multiple regression model, Standard error of estimate, Coefficient of determination, Significance of regression model, Test of significance of regression coefficients (Which variables are significant and explaining the most?), Model building, Curvilinear models, Qualitative variables, Stepwise regression, Residual analysis, Multi-colinearity.

Unit III Index Number and its Construction Models                                               5 hours

Introduction, Definition of index number, Uses of index number, Types of index number, Methods of constructing index number ,Base shifting, Deflation, Cost of living index.

Unit  IV Time Series and Forecasting Models                                                         10 hours

Index number, Understanding time series analysis, Decomposition of time series, Cyclic variation,  Seasonal variation, Deseasonalizing the time series data (Ratio to moving average method),  Choosing the appropriate forecasting technique, Moving average, Exponential smoothing, Regression based linear and curvilinear trend models, Measures of forecast accuracy (MAD,MAPE, and MSE). 

Unit V Introduction to Optimization Models                                                           12 hours

Review of Linear Programming Model: Problem formulation, Graphical solution, special cases, Duality in LP

Transportation Model: Vogel’s Approximation Method only

 Assignment Model: Hungarian Method only

Unit VI:  Network Models                                                                                        5 hours

Introduction, Critical Path Method (CPM), Project Evaluation and Review Technique (PERT), Network diagram, Probability in PERT analysis    

Text and Reference Books

Basic Books

Davis, G., & Pecar, B. Business Statistics using Excel. New Delhi: Oxford University Press

Berenson, M. L. & David M. L.  Basic Business Statistics: Concepts and Applications.  Upper Saddle River, New Jersey: Pearson Prentice Hall of USA.

Eppen, G. D., Gould, F. J. & Schmidt, C.P.  Introductory Management Science. New Delhi: Prentice Hall

References

Levin, R. I., & David S. R.  Statistics for Management. New Delhi: Prentice Hall of India.

Panneerselvam, R. Research Methodology. New Delhi: PHI Learning Private Limited.

Allbright, S. C., Winston, W., & Zappe, C. J.  Data Analysis and Decision Making with Microsoft Excel.  Pacific Grove: Duxubury Press.

Argyrous, G.  Statistics for Research with a Guide to SPSS . New Delhi: Sage South India Edition

Whigham, D. Business Data Analysis using Excel. New Delhi: Oxford University Press

 

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