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
