Understanding Statistics & Experimental Design

Related lectures (31)

Page 3 of 4

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Covers the theory of sampling, focusing on statistics for mathematicians.

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

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

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

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

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

Covers statistical hypothesis testing, likelihood estimation, and confidence intervals construction.

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.