Teaching

UMA 401, Probability and Statistics, TIET, Patiala

• Syllabus

• Text  Book, Descriptive Statistics Book 

• Lecture Notes

1. Lecture 1 Introduction to Statistics and Data Analysis

2. Lecture 2 Introduction to Probability Theory, Conditional Probability, Bayes' Theorem

3. Lecture 3 Random variables and Probability distribution (pmf/pdf/cdf)

4. Lecture 4 Random vector, Joint pmf/pdf, Marginals, and Conditional distributions

5. Lecture 5 Mean and Variance of Random Vector, Covariance, Correlation Coefficient, Regression, Chebyshev’s, Inequality, Markov                             Inequality

6. Lecture 6 Transformation of Random variable (one and two) and Its Distribution, Moments, Moment Generating function

7. Lecture 7 Discrete Distributions: Discrete Uniform, Bernoulli, Binomial, Poisson, Geometric and Negative Binomial

8. Lecture 8 Continuous Distributions: Continuous Uniform, exponential, gamma, normal, log-normal, inverse Gaussian, Cauchy, double exponential

9. Lecture 9 Sampling Distributions of Sample Mean and Sample Variance

10. Lecture 10 Estimation and Confidence Interval

11. Lecture 11 Testing of Hypotheses

• Syllabus, MST Syllabus, EST Syllabus

• Text  Book

• Lecture Notes

1. Lecture 1 Introduction to Probability Theory, Conditional Probability, Bayes' Theorem

2. Lecture 2 Random variable, PDF, PMF, CDF

3. Lecture 3 Joint random variable, conditional pdf/pmf, marginals, independent

4. Lecture 4 Mean, Variance, MGF, Covariance, Correlation, Transformation of Random Variable

• Assignment

1. Assignment 1

2. Assignment 2

• Syllabus, Quiz 1 Syllabus, Quiz 2 Syllabus, Quiz 3 Syllabus, MST Syllabus, EST Syllabus

• Text  Book

• Lecture Notes

• Syllabus, MST Syllabus, EST Syllabus

• Lecture Notes