Analysis of Variance: Principles and Applications

Related lectures (32)

Page 3 of 4

Explores the theory and applications of likelihood ratio tests in statistical hypothesis testing.

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

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

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

Explores hypothesis testing, significance levels, errors, GWAS, optimal testing, and point estimation in statistics.

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

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

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

Explores unequal variances, replication, power, effect size, biases, and their impact on research outcomes.

Explores GLM II, covering hypothesis testing, F-tests, multiple comparisons, and enriching the model to account for imaging artifacts.