Statistics Essentials: Chi-Square Test

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Page 3 of 4

Explores algebraic statistics, log-linear models, and network testing for model goodness of fit.

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Explores confidence intervals, hypothesis testing, and ROC curves in statistical analysis.

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

Explores the distribution theory of least squares estimators in a Gaussian linear model.

Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Covers hypothesis testing, types of errors, Neyman-Pearson framework, and one-sided Gaussian tests.

Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.

Explores unilateral and bilateral pairs in statistical hypothesis testing, covering critical values, test statistics, and p-values.