BBS Business Statistics

This course contains 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 .

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

Unit 1: Introduction to Statistics 
Meaning, scope and limitation of statistics, Importance of statistics in Business and Management, Types andsources of data, Methods of collection of primary and secondary data, Precautions in using; secondary data,Problems of data collection.

Unit 2: Classification and Presentation of Data
Data classification (need, meaning, objectives and types of classification); Construction of frequencydistribution and its principles; Presentation of data: Tabular presentation; Diagrammatic presentation: Bardiagram, Pie diagram; Graphic presentation: Histogram, frequency polygon, Frequency Curve and Ogive
(Illustrations related to Business and Management).

Unit 3: Measures of Central Tendency
Mean: Simple and Weighted (Arithmetic Mean, Geometric Mean and harmonic Mean), median, partitionvalues, mode, Properties of averages, choice and general limitation of an average.

Unit 4: Measures of Dispersion 
Absolute and relative measures, Range, Quartile deviation, mean deviation, standard deviation, coefficient ofvariation, Lorenz curve.

Unit 5: Skewness, Kurtosis and Moments
Meaning, objective and measurement of Skewness, Karl Pearson’s and Bowley’s Method; Five NumberSummary, Box-Whisker Plot; Kurtosis and its measurement by Percentile method; Meaning of moments,Central and Raw moments and their relationship; Measurement of Skewness and Kurtosis by momentmethod.

Unit 6: Simple Correlation and Regression Analysis 
Karl Pearson’s correlation coefficient including bi-variate frequency distribution, coefficient of determination,Probable Error, Spearman’s Rank Correlation coefficient; Concept of Linear and Non-linear regression;Simple linear regression equations including bi-variate frequency distribution, Properties of regressioncoefficients.

Unit 7: Analysis of Time Series
Meaning, need and components of time series. Measurement of trend: Semi-average, moving average, methodof least squares; Measurement of seasonal variation: Method of simple average and Ratio to moving average

Unit 8: Index Numbers
Meaning and types of Index Number; General rule and problems in construction of Index NumberMethods of constructing index numbers: Simple and weighted (Aggregative and Price Relative Method)Laspeyre’s and Paasche’s Index Number, Fisher’s Ideal Index Number; Time and Factor Reversal TestsCost of living index number (Consumer’s price index number): Aggregative Expenditure Method and FamilyBudget Method, Base shifting and Deflating

Unit 9: Probability
Definition of probability, Addition and Multiplication theorem, Application of Combination in Probability,Conditional probability and Baye’s Theorem.

Unit 10: Sampling and Estimation
Meaning of sample and population, census versus sampling, Sampling Techniques, Concept of Samplingdistribution, standard error, Estimation, estimator; Concept of types of estimates: Point and Interval

Unit 11: Quantitative Analysis
Introduction to quantitative analysis; Application of management science: Scientific approach to decisionmaking, Decision making under the condition of uncertainty and risk, Expected Profit, Expected Profit withperfect information and Expected value of perfect information, Linear Programming Problem: Problemformulation with two decision variables, Graphical solution of Maximization and Minimization problems.

Unit 12: Determinant 
Definition of determinant, Methods of finding the numerical values of determinant upto three order,Properties of determinant and its use to find the numerical values of determinants, Cramer’s Rule to solvesimultaneous equations up to three variables.

Unit 13: Matrix
Definition and types of matrix, Addition, subtraction and multiplication of matrices, Cofactors, Transpose,Adjoint and Inverse of a matrix, Inverse and Row Operations method to solve simultaneous equations uptothree unknowns. (Illustrations and applications in all chapters should be based on Business and Managementsituation as far as possible.)