Introduction to Probability and Statistics

Data, chance and inference with real rigor.

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Summaries, visualization, and exploratory analysis.

Axioms, counting, conditioning, independence, and Bayes' theorem.

Discrete and continuous distributions: binomial, Poisson, uniform, exponential, normal.

Statistics as random variables and the Central Limit Theorem.

Estimating means and proportions, margin of error, and interpretation.

Null/alternative hypotheses, p-values, errors, and t-tests.

Least squares, inference for slope, and residual diagnostics.