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Related lectures (25)

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Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Explains how to calculate the confidence interval for the score parameter in the Bernoulli model.

Explores confidence intervals, T-test, and hypothesis testing, including assumptions and critical regions.

Explores data distribution strategies, normal modeling, and checking normality with graphical tools.

Covers the characteristics and importance of the normal distribution in statistical analysis.

Covers the basics of statistical hypothesis testing, p-values, confidence levels, and t-tests.

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.

Covers the extension of the 0-1 loss to real-valued score functions and logistic regression.