21MAB301T-Probability and Statistics

Course Description

Probability & Bayes' Theorem: Basic axioms, sample spaces, conditional probability, total probability rule, independence, and Bayes’ theorem for updating beliefs

Random Variables & Distributions: Concepts of discrete and continuous random variables, PMFs, PDFs, CDFs; key distributions like binomial, Poisson, geometric, uniform, exponential, normal; expectations, variances, moments; and joint, marginal, conditional distributions

Hypothesis Testing & Estimation: Point and interval estimation, construction of confidence intervals; frequentist hypothesis tests (e.g. z-test, t-test, chi‑square), p‑value interpretation, and Bayesian inference methods

Correlation & Regression: Covariance and correlation coefficients; simple linear regression, least-squares fitting, interpretation of relationships between variables

Statistical Quality Control: Introduction to applications in monitoring and controlling process variability through control charts and other industrial statistics concepts (commonly included in engineering-focused syllabi) 

Prerequisites

NIL

Assessment Plan

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