Mathematical statistics

Related lectures (30)

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Introduces major statistical methodologies for uncovering associations between vector components in multivariate data.

Introduces Principal Component Analysis, focusing on maximizing variance in linear combinations to summarize data effectively.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Covers advanced topics in Generalized Linear Models, focusing on link functions, error distributions, and model interpretation.

Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.

Explores covariance, statistical dependence, education-fertility relationship, hypothesis testing, and comparison statistics for continuous outcomes.

Explores nonparametric statistics, covering estimation methods and the bias-variance tradeoff.