Linear Statistical Models

A collection of introductory ideas in Linear Statistical Models.

Content by idea can be found below.

2 Linear Statistical Models 0 Matrix Inverse

Matrix Inverse

An introduction to outcomes, outcome spaces, events, probability, mutually exclusive events, exhaustive events, set laws, conditional probability, partitions of the outcome space, the addition law, the multiplication law (Bayes’ Theorem), and the law of total probability.

2 Linear Statistical Models 1 Symmetric Idempotence

Symmetric Idempotence

An introduction to independent events, random variables, state spaces, countable sets, discrete random variables, probability mass functions, and expected value.

2 Linear Statistical Models 2 Random Vectors

Random Vectors

An introduction to continuous random variables, probability density functions, cumulative density functions, expected value, variance, and standard deviation.

2 Linear Statistical Models 3 Linear Models

Linear Models

An introduction to tail moments, Bernoulli random variables, binomial random variables, binomial shape, and geometric random variables.

2 Linear Statistical Models 4 Normal Random Vectors

Normal Random Vectors

An introduction to negative binomial random variables, negative binomial shape, hypergeometric random variables, and Poisson random variables.

Thank you for reading to the end.