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Statistical Methods Intro Lecture (Day 1)
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Classroom Contents
Statistical Methods
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- 1 Statistical Methods Intro Lecture (Day 1)
- 2 Statistical Methods: Intro to Probability and Counting
- 3 Conditional Probability, Bayes' Theorem, and Independence
- 4 Independent Events Continued and Bayes' Theorem Example
- 5 Random Variables, Probability Distributions, Cumulative Distribution Functions
- 6 Discrete CDFs, Expected Values, and Binomial Distribution Intro
- 7 Discrete Distributions:: Binomial, Negative Binomial, Hypergeometric, and Poisson
- 8 Statistical Methods: Review Problems For Discrete Distributions
- 9 Continuous Random Variables, PDFs, Uniform Dist, CDFs
- 10 Continuous Random Variables, Expected Values, and the Normal Distribution
- 11 Continuous Distributions: Exponential, Gamma, Weibull, Lognormal, and Beta. Also Joint Probability
- 12 Joint Distributions, Continuous Random Variables, Expected Values and Covariance
- 13 Covariance and Correlation and the Central Limit Theorem
- 14 Confidence Intervals for the Mean and Population Proportion.
- 15 Full Lecture: Confidence Intervals for Standard Deviation and General Hypothesis Testing
- 16 Hypothesis Testing Lecture Continued
- 17 Statistical Methods Exam 2 Review Problems
- 18 Statistical Methods Exam 2 Review Problems Continued
- 19 Hypothesis Testing Continued (Single and Two Sample Tests)
- 20 Two Sample Hypothesis Tests Continued | Paired Data and Population Proportions
- 21 Single Factor Anova Lecture
- 22 Linear Regression Intro Lecture: Calculus Based Lecture
- 23 Hypothesis Testing Linear Regression Parameters
- 24 Statistical Methods Exam 3 Review Problems
