Course Contents

Concept of Random Variables and their distributions, Discrete and Continuous random variables, Discrete probability distribution, Probability functions, joint distribution, Continuous probability distribution, Probability density functions Independence of two random variables. Mathematical Expectation, Expectation of a function of random variables and its properties (without proof), Mean, Variance and Moments of simple discrete and continuous probability distributions, Covariance of two random variables. Binomial distribution with application, its fittings to statistical data. Normal distribution (properties without proof) Its fitting to statistical data. Normal approximation to binomial distribution. Case Study : Analysis of real data based on the above contents, presented in the form of a report.

Course Synopsis

Introduction to the concept of random variable and probability distribution. Application of some important discrete and continuous probability distributions.

Course Learning Outcomes

On completion of the course students should be able to differentiate between discrete and continuous random variables and also find the mean & spread out of it. They also will solve the problems relating different probability distributions.