Statistical Methods

Dive into a comprehensive 23-hour lecture series on statistical methods covering probability, distributions, hypothesis testing, and regression analysis. Learn about conditional probability, Bayes' theorem, random variables, discrete and continuous distributions, confidence intervals, ANOVA, and linear regression. Explore practical applications through review problems and exam preparation sessions. Gain a solid foundation in statistical concepts and techniques essential for data analysis and research across various fields.

Statistical Methods Intro Lecture (Day 1).
Statistical Methods: Intro to Probability and Counting.
Conditional Probability, Bayes' Theorem, and Independence.
Independent Events Continued and Bayes' Theorem Example.
Random Variables, Probability Distributions, Cumulative Distribution Functions.
Discrete CDFs, Expected Values, and Binomial Distribution Intro.
Discrete Distributions:: Binomial, Negative Binomial, Hypergeometric, and Poisson.
Statistical Methods: Review Problems For Discrete Distributions.
Continuous Random Variables, PDFs, Uniform Dist, CDFs.
Continuous Random Variables, Expected Values, and the Normal Distribution.
Continuous Distributions: Exponential, Gamma, Weibull, Lognormal, and Beta. Also Joint Probability.
Joint Distributions, Continuous Random Variables, Expected Values and Covariance.
Covariance and Correlation and the Central Limit Theorem.
Confidence Intervals for the Mean and Population Proportion..
Full Lecture: Confidence Intervals for Standard Deviation and General Hypothesis Testing.
Hypothesis Testing Lecture Continued.
Statistical Methods Exam 2 Review Problems.
Statistical Methods Exam 2 Review Problems Continued.
Hypothesis Testing Continued (Single and Two Sample Tests).
Two Sample Hypothesis Tests Continued | Paired Data and Population Proportions.
Single Factor Anova Lecture.
Linear Regression Intro Lecture: Calculus Based Lecture.
Hypothesis Testing Linear Regression Parameters.
Statistical Methods Exam 3 Review Problems.