Statistical package in python (pandas-based): https://github.com/raphaelvallat/pingouin/
MCMC: https://machinelearningmastery.com/markov-chain-monte-carlo-for-probability/
DISCRETE PROBABILITY DISTRIBUTIONS
- Bernoulli distribution (single trial with 2 possible outcomes)
- If X is the random variable defining the outcome of the trial and p is the probability of success (of 1 outcome), it follows a distribution X ~ Bernoulli(p, p(1-p))
- where p is the mean and p(1-p) is the variance
- Binomial distribution (n identical trials with 2 possible outcomes in each)
- X ~ Binomial(np, np(1-p)) where this distribution can be thought of as multiple independent Bernoulli trials
- Poisson distribution (large number of rare events with unlimited outcomes) X ~ Poisson(l) where l is both mean and variance of unlimited occurrences https://mathworld.wolfram.com/PoissonDistribution.html
CONTINUOUS PROBABILITY DISTRIBUTIONS
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Uniform distribution (outcomes equally likely)
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Standard Normal/Gaussian distribution The mean, median, and mode of the distribution coincide. The curve of the distribution is bell-shaped and symmetrical about the line x = μ. The total area under the curve is 1. Exactly half of the values are to the left of the center and the other half to the right.
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Exponential distribution (continuous and independent events in a time interval)
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Weibull distribution
Understanding slope, intercept and error terms in LINEAR REGRESSION
MULTIPLE LINEAR REGRESSION
