Experimental Design in Biostatistics

Related lectures (31)

Page 3 of 4

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

Introduces t-tests, focusing on hypothesis testing and comparing coefficients using the t-distribution.

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

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

Explores Principal Component Analysis, dimensionality reduction, data quality assessment, and error rate control.

Explores principal components, covariance, correlation, choice, and applications in data analysis.

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

Covers the essentials of ANOVA, explaining its concept, calculations, assumptions, and interpretation of results.

Covers t-tests, p-values calculation, and comparison of coefficients.