Statistical Tests: Independence and Homogeneity

Related lectures (31)

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Explains the Chi-Square test for independence hypothesis and its practical applications.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Covers hypothesis testing, confidence intervals, and their applications in statistics.

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

Provides an overview of basic probability theory, ANOVA, t-tests, central limit theorem, metrics, confidence intervals, and non-parametric tests.

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.

Covers hypothesis testing, Q-Q plots, and non-parametric tests in statistics.

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Explains the two-sample t-test for comparing means of independent samples, including hypothesis testing steps and test statistic calculation.