Lecture 1: Introduction & Measures of Central Tendency, Slides, (Extra Material)

Lecture 2: Measures of Dispersion & Chebyshev’s Inequality, Slides, (Example)

Lecture 3: Normal Datasets & Correlation, Slides, (Correlation)

Lecture 4: Counting Principles, Slides, (Examples)

Lecture 5: Sample Space & Events, Slides, (Sample Spaces)

Lecture 6: Probability, Slides, (Conditional Probability, Multiplication Rule)

Lecture 7: Bayes' Theorem, Slides, (Bayes' Rule, Examples)

Lecture 8: Random Variables, Slides, (Examples)

Lecture 9: Expectation and its Properties, Slides, (Joint PDF and CDF)

Lecture 10: Covariance and Some Important Results, Slides, (WLLM, simulation)

Lecture 11: Discrete Distirbutions, Slides, (Bernoulli and Binomial, Example)

Lecture 12: Discrete Distributions, Slides, (Example, Link between Binomial and Poisson, Taylor Series)

Lecture 13: Continuous Distributions, Slides, (Uniform, Exponential)

Lecture 14: Functions of One Random Variable, Slides, (Examples)

Lecture 15: Continuous Distributions, Slides, (Normal distribution, Normal to Standard Normal, Reading Normal Tables)

Lecture 16: Continuous Distributions, Slides, Simulation, (Simulating Observations)

Lecture 17: Sampling Distribution, Slides, Simulation, (t-distribution, CLT, Example)

Lecture 18: Sampling Distribution, Slides

Lecture 19: Point and Interval Estimation, Slides, (Notes, CI for means)

Lecture 20: Point and Interval Estimation, Slides, (t-score, CI for proportions)

Lecture 21: Point and Interval Estimation, Slides, (CI for diff in means)

Lecture 22: Hypothesis Testing, Slides, (Test of means, Test of proportions)

Lecture 23: Errors in Hypothesis Testing, Slides, (Errors, Power)

Lecture 24: T-test and Paired T-test, Slides, (t-test, Paired t-test)