Multilinear Regression: Standardized Variables and Effects

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Page 3 of 4

Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.

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

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Explains the least-squares regression line, correlation coefficients, outliers, influential points, and residuals in regression models.

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Explores non-linear regression models, likelihood estimation, model fitting, and confidence intervals.

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

Explores the distribution theory of least squares estimators in a Gaussian linear model.