Hypothesis Testing & Confidence Intervals

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 3 of 3

Confidence Intervals and MLE Limit Theorems

Explores constructing confidence intervals and MLE limit theorems for large samples.

Sampling: Inference and Statistics

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Confidence Intervals: Gaussian Estimation

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Distribution Theory of Least Squares

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

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Hypothesis Testing: A Different Perspective

Delves into a different perspective on hypothesis testing, emphasizing the p-value and significance levels.

Understanding Statistics & Experimental Design

Covers basic probability theory, signal detection theory, statistics, and meta-statistics, explaining effect sizes, power, and hypothesis testing.

Estimation Criteria

Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.

Estimating Parameters: Confidence Intervals

Explores estimating parameters through confidence intervals in linear regression and statistics.

Likelihood Ratio Tests: Optimality and Extensions

Covers Likelihood Ratio Tests, their optimality, and extensions in hypothesis testing, including Wilks' Theorem and the relationship with Confidence Intervals.