Estimating R: Multiple Testing and P-values

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 3 of 4

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Likelihood Ratio Test: Neyman-Pearson Lemma

Explores likelihood ratio tests and the Neyman-Pearson Lemma for statistical hypothesis testing.

Hypothesis Testing in Statistics

Delves into hypothesis testing, covering test statistics, critical regions, power functions, p-values, multiple testing, and non-parametric statistics.

Hypothesis Testing: State of Nature

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

Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Hypothesis Testing in Statistics

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

Statistical Hypothesis Testing: Unilateral and Bilateral Pairs

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

Hypothesis Testing: Q-Q Plots and Non-Parametric Tests

Covers hypothesis testing, Q-Q plots, and non-parametric tests in statistics.

Estimation of Theoretical Frequencies

Covers the estimation of theoretical frequencies and independence testing between two characteristics, with examples for practical application.

Data Analysis: Correlation Measures

Covers the basics of data analysis, focusing on statistical concepts and correlation measures.