Probability and Statistics Review
2
Probability Models
Statistical Computing
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Introduction
preface.html
Probability and Statistics Review
1
Random Variables
2
Probability Models
3
Statistics
Monte Carlo Methods
4
Monte Carlo Methods
5
Markov Chain Monte Carlo Methods
6
Monte Carlo Integration
Randomizations
7
Permutation Tests
8
Permutation Regression
The Boostrap Method
9
Parametric Bootstrapping
10
Nonparametric Bootstrapping
Simulation-based Analysis
11
Monte Carlo Hypothesis Testing
12
Monte Carlo Methods for Regression Models
13
Monte Carlo Power Analysis
R Programming
14
Basic R Programming
15
Data Summarization
16
Graphics
17
Control Flow
18
Functional Programming
19
Scripting and Piping in R
20
Further Resources
Table of contents
2.1
Bernoulli Model
2.1.1
Distribution Functions
2.1.2
Expected Value
2.1.3
Variance
2.2
Binomial Model
2.2.1
Distribution Functions
2.2.2
Expected Value
2.2.3
Variance
2.3
Poisson Model
2.3.1
Distribution Functions
2.3.2
Expected Value
2.3.3
Variance
2.4
Negative Binomial Model
2.4.1
Distribution Functions
2.4.2
Expected Value
2.4.3
Variance
2.5
Multinomial Model
2.5.1
Distribution Functions
2.5.2
Expected Value
2.5.3
Variance
2.6
Uniform Model
2.6.1
Distribution Functions
2.6.2
Expected Value
2.6.3
Variance
2.7
Normal Model
2.7.1
Distribution Functions
2.7.2
Expected Value
2.7.3
Variance
2.8
Gamma Model
2.8.1
Distribution Functions
2.8.2
Expected Value
2.8.3
Variance
2.9
Beta Model
2.9.1
Distribution Functions
2.9.2
Expected Value
2.9.3
Variance
2.10
Weibull Model
2.10.1
Distribution Functions
2.10.2
Expected Value
2.10.3
Variance
Probability and Statistics Review
2
Probability Models
2
Probability Models
2.1
Bernoulli Model
2.1.1
Distribution Functions
2.1.2
Expected Value
2.1.3
Variance
2.2
Binomial Model
2.2.1
Distribution Functions
2.2.2
Expected Value
2.2.3
Variance
2.3
Poisson Model
2.3.1
Distribution Functions
2.3.2
Expected Value
2.3.3
Variance
2.4
Negative Binomial Model
2.4.1
Distribution Functions
2.4.2
Expected Value
2.4.3
Variance
2.5
Multinomial Model
2.5.1
Distribution Functions
2.5.2
Expected Value
2.5.3
Variance
2.6
Uniform Model
2.6.1
Distribution Functions
2.6.2
Expected Value
2.6.3
Variance
2.7
Normal Model
2.7.1
Distribution Functions
2.7.2
Expected Value
2.7.3
Variance
2.8
Gamma Model
2.8.1
Distribution Functions
2.8.2
Expected Value
2.8.3
Variance
2.9
Beta Model
2.9.1
Distribution Functions
2.9.2
Expected Value
2.9.3
Variance
2.10
Weibull Model
2.10.1
Distribution Functions
2.10.2
Expected Value
2.10.3
Variance
1
Random Variables
3
Statistics