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Sunday, December 30, 2018

Gauhati University - M.Com Distance Syllabus: Business Statistics


1.4 Business Statistics
Unit I: Probability and Probability Distributions: Probability: Various approaches to probability, dependent and independent events, conditional probability, Baye's rule, importance of probability theory in decision making; mathematical expectation & variance of a random variable, laws of expectation; concept of probability distribution, normal probability distribution.

Unit II: Sampling Distribution, Theory of Estimation and Testing of Hypothesis Sampling distribution of a statistic and its standard error, statement of Central Limit Theorem, estimation of the mean and the variance of the sampling distribution of the sample means; Testing of hypothesis: Type I and Type II errors, one tailed and two tailed tests, tests based on standard normal test, 't' test, chi-square (x2) test and F-test.

Unit III: Partial and Multiple Correlation and Regression, Association of Attributes: Concept of partial and multiple correlation and regression, various formulate and problems; association of attributes: Concept, order of a class, class frequency, consistency of data, kinds of association of attributes, methods of measuring association between two attributes, partial association.

Unit IV: Business Forecasting: Steps in business forecasting, methods of business forecasting: Naive method barometric method, econometric method, utility and reliability of business forecasting.

Unit V: Measures of Inequality: The variance and the coefficient of variation, the standard deviation of logarithms, the Lorenz curve and Gini coefficient.

Unit VI: Statistical Quality Control (SQC): Meaning of SQC, process control; control charts: X, R, P and C charts, product control; single and double sampling inspection plans, concepts of OC curve, AQL, LTPD, AOQ and AOQL.

Unit VI: Decision Theory: Steps in decision making environments, decision making under conditions of uncertainty, decision making under conditions of risk, decision trees, advantages and limitations of decision trees.

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