Regression Analysis: Multivariate Data Modeling

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 1 of 4

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Linear Regression: Least Squares

Delves into linear regression, emphasizing least squares estimation, residuals, and variance.

Linear Regression: Estimation and Inference

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

Bivariate Data Analysis: Correlation and Regression

Explores bivariate data analysis, correlation, and regression techniques for model assessment.

Linear Regression: Beyond the Basics

Explores advanced concepts in linear regression models, including multicollinearity, hypothesis testing, and handling outliers.

Linear Regression: Maximum Likelihood Approach

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Linear Regression Basics

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

Applied Biostatistics: Bivariate Data and Regression Analysis

Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.