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MATH 345 - Probability (Kuter)

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1: What is Probability
1.1 Sample Spaces and Events
1.2: Probability Measures
2: Computing Probabilities
2.1: Equally Likely Outcomes and Counting Techniques (Combinatorics)
2.2: Conditional Probability and Bayes' Rule
2.3: Independent Events
3: Discrete Random Variables
3.1: Introduction to Random Variables
3.2: Probability Mass Functions (PMFs) and Cumulative Distribution Functions (CDFs) for Discrete Random Variables
3.3: Bernoulli and Binomial Distributions
3.4: Hypergeometric Geometric and Negative Binomial Distributions
3.5: Poisson Distribution
3.6: Expected Value of Discrete Random Variables
3.7: Variance of Discrete Random Variables
3.8: Moment-Generating Functions (MGFs) for Discrete Random Variables
4: Continuous Random Variables
4.1: Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) for Continuous Random Variables
4.2: Expected Value and Variance of Continuous Random Variables
4.3: Uniform Distributions
4.4: Normal Distributions
4.5: Exponential and Gamma Distributions
4.6: Weibull Distributions
4.7: Chi-Squared Distributions
4.8: Beta Distributions
5: Probability Distributions for Combinations of Random Variables
5.1: Joint Distributions of Discrete Random Variables
5.2: Joint Distributions of Continuous Random Variables
5.3: Conditional Probability Distributions
5.4: Finding Distributions of Functions of Continuous Random Variables
5.5: Sample Mean