Distribution Theory of Least Squares

Related lectures (31)

Page 2 of 4

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Explores the optimality of the Least Squares Estimator and its large sample distribution.

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

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

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

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

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Explores the IRLS algorithm for weighted least squares estimation in GLM.

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