Understanding Statistics & Experimental Design

Related lectures (30)

Page 3 of 3

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

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Introduces comparison statistics, error rates, hypothesis testing, and real-life examples of treatment effectiveness and weightlifting analysis.

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

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

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Explores t-tests, z-tests, and empirical tests for sample comparison and hypothesis testing.

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Explores variance, covariance, and practical applications in statistics and probability.

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