Introduction to probability, counting techniques, dependent and independent events, conditional probability, additive rule of probability and its applications. Contingency tables, joint and marginal probabilities, the multiplication rule, Baye’s theorem, applications.

Concept of random variable, Discrete probability distribution, Case study. Continuous probability distribution with examples, Probability density function, Joint probability distribution, Examples, Mean of a random variable. Variance of a random variable. Binomial distribution. Mean and variance of Binomial distribution. Examples, Poisson distribution, Normal distribution, area under the normal curve, Standard Normal distribution, inverse use of table of areas under the normal curves, applications.

Goodness of fit, fitting a straight, parabola and higher degree curves.

Regression analysis by least squares method, testing the signification of b, simple correlation analysis, coefficient of correlation & coefficient of determination, testing the significance of r.

Sampling distribution of means with replacement and without replacement. Central Limit theorem, examples.

Testing a Statistical Hypothesis, Type I & II error, One tailed and Two tailed tests, tests concerning means and variances, testing the difference between two means. Good-ness of Fit test, test of independence, applications.

Confidence interval of one population mean, estimating a population mean, estimating the difference between two population means. The Chi - square distribution, estimating a population standard deviation. The F distribution, estimating the ratio of two variances, applications.

The essence of time series analysis in business, measurement of trend (T), measurement of seasonal variations (S), measurement of cyclical variations (C), random variation (R), Applications.