What you'll learn

36 lessons in Statistics

Descriptive statisticsHypothesis testingLinear regressionSampling distributionsConfidence intervalsTwo-sample tests & ANOVACorrelation vs causationMaximum likelihood estimationMethod of momentsChi-square testsBootstrap & resamplingSufficient statistics & Fisher informationExponential familiesMCMC: Metropolis-Hastings & GibbsGeneralized linear models (GLMs)Proof: OLS minimizes squared errorProof: unbiasedness of $s^2$Sketch: Central Limit TheoremMultivariate statistics: PCA, factor analysisTime series basicsPoint estimation: bias, MSE & consistencyThe Cramér–Rao lower boundThe Neyman–Pearson lemmaConjugate priors & Bayesian estimationNonparametric testsMultiple testing: Bonferroni & FDRExperimental designSurvival analysis & Kaplan–MeierStatistical decision theoryAsymptotic statistics & the delta methodThe EM algorithmCausal inferenceStatistical learning theoryRobust statisticsSequential analysis & the SPRTModel selection: AIC, BIC & cross-validation

How Erudia teaches

Built to be understood — and remembered.

Every idea is taught with motivation and a worked example before the drills, and an FSRS spaced-repetition engine schedules each review for the moment just before you'd forget it. A short placement check finds what you already know, so you start Statistics exactly where it's useful.

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