Quantitative Techniques by Pravesh Kumar Singh MBA 1st sem

Quantitative Techniques by Pravesh Kumar Singh MBA 1st sem

MB 106 QUANTITATIVE TECHNIQUES

Module - I                                                                                                                             

1)  Linear Programming: Formulating Maximization/minimization Problems, Graphical Solution, Simplex Method, Artificial Variables – Big M – Method, Special Cases of LP, Duality of LP and its Interpretation, Post Optimality/Sensitivity Analysis, Applications of LP.                                                                     

2) Transportation Problems: Introduction – Mathematical Formulation of Transportation Problem – the Transportation Method for Finding Initial Solutions-North West Corner Method – Least Cost Method – Vogel’s Approximation Method – Test for Optimality – Steps of MODI Method-loops in Transportation Table – Degeneracy.                                                                                                                                   

3) Assignment Problems: Introduction – Mathematical Statement of the Problem-Hungarian Method of Solution – Maximization case in Assignment Problem – Unbalanced Assignment Problem – Restrictions on Assignment – Travelling Salesman Problem.                                                                                                       

4) Theory of Games: Introduction-Two Person Zero Sum Games – Pure Strategies – Games with Saddle Points – Rules to Determine Saddle Points – Mixed Strategies – Game Without Saddle Points – the Rules of Dominance – Methods of Solution for Games Without Saddle Points – Algebraic Methods, Graphical Methods. 

Module - II                                                                                                                       

5) Basic Statistics: Basic Concept (Variables, Population v/s Sample, Central tendency, Dispersion, Data Visualization, Simple Correlation and Regression.                                                          

6) Probability & Distribution: Probability – Introduction, Rules of Probability, Conditional Probability (Baye’s Theorem), Random Variables, Discrete and Continuous Distributions (Binomial, Poisson and Normal), Sampling – Types and Distribution.                                                                                                                

7) Theory of Estimation: Estimation – Estimation Problems, Standard Error, Margin of Error, Confidence Error, Confidence Interval, Characteristics of Estimators, Consistency Unbiasedness, Sufficiency and Efficiency, most Sufficient Estimators. Point Estimation and Interval Estimation.                                           

8) Statistical Inference: Hypothesis Testing, Parametric Test – Z, F, t Test, ANOVA, Non Parametric Test – Chi Square Test (Goodness of Fit, Independence of Attributes) Spearman’s Rank Correlation coefficient.