Difference between revisions of "Statistics notes - on random variables, distributions, probability"

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m (A few distributions)
m (Poisson distribution)
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independently of time since last even.
independently of time since last even.
Used mainly if the events are relatively sparse; at higher rates it tends towards rh
Used mainly if the events are discrete, and relatively sparse,
where the distribution sits against zero and noticeably isn't very symmetric.
Note that when not so sparse, the shape much resembles a normal distribution.
In part just because of the CLT, though, and it may still make more sense to model it as Poisson for a few usually-minor reasons (normal is continuous).
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===Gaussian/Normal distribution===
===Gaussian/Normal distribution===

Revision as of 19:03, 4 July 2020

This is more for overview of my own than for teaching or exercise.

Overview of the areas

Arithmetic · 'elementary mathematics' and similar concepts
Set theory, Category theory
Geometry and its relatives · Topology
Elementary algebra - Linear algebra - Abstract algebra
Calculus and analysis
 : Information theory · Number theory · Decision theory, game theory · Recreational mathematics · Dynamical systems · Unsorted or hard to sort

Math on data:

  • Statistics as a field
some introduction · areas of statistics
types of data · on random variables, distributions
Virtues and shortcomings of...
on sampling · probability
glossary · references, unsorted
Footnotes on various analyses

Other data analysis, data summarization, learning

Regression · Classification, clustering, decisions · dimensionality reduction · Optimization theory, control theory
Connectionism, neural nets · Evolutionary computing

This article/section is a stub — probably a pile of half-sorted notes, is not well-checked so may have incorrect bits. (Feel free to ignore, fix, or tell me)

Dependent versus independent

Random variables, distributions

Probability function(/distribution)

Probability mass function (discrete)

Probability density functions (continuous)

Cumulative distribution function

Expected value

A few distributions

Binomial distribution

Poisson distribution

Gaussian/Normal distribution