Teaching

UMA401 Probability and Statistics

UMA 401, Probability and Statistics, TIET, Patiala

Lecture 1 Introduction to Statistics and Data Analysis
Lecture 2 Introduction to Probability Theory, Conditional Probability, Bayes' Theorem
Lecture 3 Random variables, pmf/pdf/cdf, Expectation, Variance, MGF, Markov and Chebyshev’s Inequality
Lecture 4 Discrete and Continuous Probability Distributions
Lecture 5 Random vector, Joint pmf/pdf, Marginals, and Conditional distributions, and conditional Mean and Variance, independent, Covariance, Correlation Coefficient, Regression
Lecture 6 Sampling Distributions of Sample Mean and Sample Variance, CLT, t, F, chi-square Distribution
Lecture 7 Estimation and Confidence Interval
Lecture 8 Testing of Hypotheses

Lecture 1 Introduction to Probability Theory, Conditional Probability, Bayes' Theorem
Lecture 2 Random variable, PDF, PMF, CDF
Lecture 3 Joint random variable, conditional pdf/pmf, marginals, independent
Lecture 4 Mean, Variance, MGF, Covariance, Correlation, Transformation of Random Variable
Lecture 5 Binomial, Poisson, Geometric, Exponential, Normal, t, F and Chi square Distribution
Lecture 6 Testing of Hypothesis (Short notes only formulas)
Lecture 7 ANOVA (One-Way and Two-Way)
Lecture 8 Time Series Analysis
Lecture 9 Stochastic Process

Lecture 1: Introduction to Probability
Lecture 2: Random variable, PDF, PMF, CDF
Lecture 3: Joint PDF, PMF, Marginals, Conditional
Lecture 4: Mean, Variance, MGF, Covariance, Independent RV, Markov and Chebyshev inequalities
Lecture 5: Discrete and continuous distribution, The CLT