Business Statistics

Data classification (need, meaning, objectives and types of classification); Construction of frequency distribution 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).

Mean: Simple and Weighted (Arithmetic Mean, Geometric Mean and harmonic Mean), median, partition values, mode, Properties of averages, choice and general limitation of an average.

Absolute and relative measures, Range, Quartile deviation, mean deviation, standard deviation, coefficient of variation, Lorenz curve.

Meaning, objective and measurement of Skewness, Karl Pearson’s and Bowley’s Method; Five Number Summary, 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 moment method.

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 regression coefficients.

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

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 Price Relative Method) Laspeyre’s and Paasche’s Index Number, Fisher’s Ideal Index Number; Time and Factor Reversal Tests Cost of living index number (Consumer’s price index number): Aggregative Expenditure Method and Family Budget Method, Base shifting and Deflating

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

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

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

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 solve simultaneous equations up to three variables.

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 upto three unknowns. (Illustrations and applications in all chapters should be based on Business and Management situation as far as possible.)