Business Statistics

Syllabus

BUSINESS STATISTICS

MODULE 1: INTRODUCTION TO STATISTICS                                                  

Definition, Importance of Statistics; Statistical Data – Sources and Types - Classification of data, Frequency Distribution, Diagrammatic and Graphic Representation - Histograms, Frequency Polygon, Cumulative Frequency Curves or Ogives, Numerical descriptive techniques: Measures of Central Tendencies. Measures of Variability - Range, Standard Deviation, Variance, and Coefficient of Variance; Skewness—Karl Pearson’s Co-efficient of Skewness, Bowley’s Co-efficient of Skewness.

MODULE 2: TIME SERIES ANALYSIS AND INDEX NUMBERS                      

Time Series:Introduction, Objectives of Time Series, Identification of Trend - Methods of measuring: Semi averages, Moving averages, Method of Lease squares, Non-linear trend. Application of time series in business.

Index numbers: Meaning, types and uses of Index numbers, Construction of Price, Quantity and Value indices, fixed base and Chain base method. TRT& FRT test. Consumer price index.  

MODULE 3:   CORRELATION AND REGRESSION ANALYSIS                       

Introduction and significance, Scatter diagram, Karl Pearson’s coefficient of Correlation for Uni-variate and Bi-variate series, Spearman’s Rank Correlation. Regression analysis: Regression equations.

MODULE 4: HYPOTHESIS TESTING, PARAMETRIC & NON PARAMETRIC TESTS         

Hypothesis Testing, Formulation of Hypotheses, Type I and II error, z-test, t-test, f-test and Chi-Square test, Analysis of Variance(ANOVA) -one and two way. Design of experiments, Non-parametric tests – Sign test, Wilcoxon test, Mann-Whitney U test, Median test, Run test and Kolmogorov –Smirnov one sample test

MODULE 5: THEORY OF PROBABILITY                                                                              

Concept and Definition - Relevance to Management Decisions law of independence - Sample Space and Events – Union of events, Relevance of Permutations and Combinations to Probability - Rules of Probability, Bayes’ theorem & its applications, basics of Random Variables and Concept of Probability Distribution. Theoretical Probability Distributions: Binomial, Poisson and Normal.

MODULE 6: DECISION THEORY

Decision Theory – Decision under certainty, Decision making under risk (EMV criteria) and Decision making under uncertainty. Decision tree (Problems).