In Part I of this series, we cover basic statistical inference and experimentation, focusing on: basic statistics; derivation and review of key distributions and their relations; hypothesis testing, including an in depth power analysis for the chi-squared statistic; experimentation, including A/B tests, stratification, one- and two-factor experiments, and an introduction to bandit algorithms; maximum likelihood; gradient descent; introduction to survival analysis and stochastic processes, including empirical ...
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In Part I of this series, we cover basic statistical inference and experimentation, focusing on: basic statistics; derivation and review of key distributions and their relations; hypothesis testing, including an in depth power analysis for the chi-squared statistic; experimentation, including A/B tests, stratification, one- and two-factor experiments, and an introduction to bandit algorithms; maximum likelihood; gradient descent; introduction to survival analysis and stochastic processes, including empirical estimation of online survival and event processes. The theory is illustrated with simulations in Python throughout the text.
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