Business Statistics Syllabus - BBS (TU)

Course Objective

The basic objective of this course is to provide students an understanding of concepts of business statistics and to acquaint the students with necessary statistical and mathematical tools and techniques to be used in business decision-making processes.

Course Description

This course comprises introduction to statistics, classification and presentation of data, measures of central tendency, measures of dispersion, Skewness, kurtosis and moments, simple correlation and regression analysis, analysis of time series, index numbers, probability, sampling and estimation, quantitative analysis, determinant and matrix.

Learning outcomes

On the completion of this course, students will be able to:

• classify, present and analyze the business and management related
• interpret the data from the management
• apply forecasting techniques in business and management.
• use quantitative techniques in different decision making
• apply the tool for cause and effect relationship in managerial decision.

Course Details

Unit 1: Introduction to Statistics                                                                              5 LHs

Meaning, scope and limitation of statistics; Importance of statistics in business and management; Types and sources of data; Methods of collection of primary and secondary data; Precautions in using secondary data; and Problems of data collection.

Unit 2: Classification and Presentation of Data                                                      5 LHs

Data classification: meaning, need, objectives and types of classification; Construction of frequency distribution and its principles; Presentation of data: tabular presentation, diagrammatic presentation: bar diagram, Pie diagram, graphic presentation: histogram, frequency polygon, frequency curve and ogive.

(Illustrations related to Business and Management).

Unit 3: Measures of Central Tendency                                                                  15 LHs

Mean: simple and weighted (Arithmetic Mean, Geometric Mean and Harmonic Mean);

Median; Partition values; Mode; Properties of averages; and Choice and general limitations of an average.

Unit 4: Measures of Dispersion                                                                             15 LHs

Absolute and Relative measures; Range; Quartile deviation; Standard deviation; Coefficient of variation; and Lorenz curve.

Unit 5: Skewness, Kurtosis and Moments                                                              15 LHs

Meaning, objective and measurement of Skewness; Karl Pearson’s and Bowley’s methods; Five number summary; Box-Whisker Plot; Kurtosis and its measurement by Percentile method; Meaning of moments; Central and raw moments and their relationship; and Measurement of Skewness and Kurtosis by method of moment.

Unit 6: Simple Correlation and Regression Analysis                                            15 LHs

Karl Pearson’s correlation coefficient including bi-variate frequency distribution; Coefficient of determination; Probable Error(P.E.); Spearman’s rank correlation coefficient; Concept of linear and non-linear regression; Simple linear regression equations including bi-variate frequency distribution; and Properties of regression coefficients.

Unit 7: Analysis of Time Series                                                                            15 LHs

Meaning, need and components of time series; Measurement of trend: semi-average, moving average, method of least squares; Measurement of seasonal variation: method of simple average and ratio to moving average (only quarters).

Unit 8: Index Numbers                                                                                           15 LHs

Meaning and types of Index Number; General rule and problems in construction of index number; Methods of constructing index numbers: simple and weighted (aggregative and average of price relative method), Laspeyre’s index number, Paasche’s index number, Fisher’s ideal index number; and Time and factor reversal tests.

Cost of living index number (or Consumer’s price index number): aggregative expenditure

method and family budget method; and Base shifting and deflating.

Unit 9: Probability                                                                                                 10 LHs

Definition of probability; Addition and multiplication theorem; Application of combination rule in probability; and Conditional probability.

Unit 10: Sampling and Estimation                                                                            5 LHs

Meaning of sample and population; Census versus sampling; Sampling techniques; Concept of sampling distribution; Standard error; Estimation: estimator, concept of types of estimates: Point and Interval estimates.

Unit 11: Quantitative Analysis                                                                               15 LHs

Introduction to Quantitative Analysis; Application of management science: scientific approach to decision making; Decision making under the condition of uncertainty: maximax

(optimism), maximin(pessimism), minimax regret; Decision making under risk : Expected Profit (EP) Or Expected Monetary value (EMV), Expected Profit with perfect Information (EPPI), Expected Value of Perfect Information (EVPI); Linear Programming Problem (LPP): Problem formulation with two decision variables, graphical solution of maximization and minimization problems.

Unit 12: Determinant                                                                                               10 LHs

Definition of determinant; Methods of finding the numerical values of determinant up to three order; Properties of determinant and its use to find the numerical values of determinants; and Cramer’s rule to solve simultaneous equations up to three variables.

Unit 13: Matrix                                                                                                        10 LHs

Definition and types of Matrix; Addition, subtraction and multiplication of matrices; Cofactors; Transpose; Adjoint and inverse of a matrix; Matrix method to solve simultaneous equations up to three unknown variables.

Note: Illustrations and applications in all chapters should be based on Business and Management situation as far as possible.

Suggested Readings:

1. Gupta, C., Fundamentals of Statistics for Management, Mumbai: Himalayan Publishing House.
2. Tulsian, C. & Pandey, Vishal, Quantitative Techniques: Theory and Problems, New Delhi:Pearson Education.
3. David, . Levine, Timothy, C. Krehbiel, & Mark, L. Berenson , Business Statistics A first

course, USA:Prentice Hall.

4. Gupta , P., Statistical Methods, New Delhi:Sultan Chand and Sons.
5. Levine, Richard I, and Dabid S. Rubin, Statistics for Management , New Delhi: Prentice hall of India Pvt.
6. Aitken ,A.C. , Determinant and Matrices, UK: Amazon.