Regression Analysis: Model Selection and Diagnostic Tools

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Related lectures (32)

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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: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Regression Analysis: Interpretation and Applications

Explores multiple regression, ANOVA, interpretation of coefficients, and regression estimation.

Applied Biostatistics: Bivariate Data and Regression Analysis

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

Regression Diagnostics

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.

House Price Regressions: Exploring OLS Assumptions

Explores OLS regressions for house prices, covering outliers, influential observations, model specification, and selection strategies.

Linear Regression: Ozone Data Analysis

Explores linear regression analysis of ozone data using statistical models.

Linear Regression: Estimation and Inference

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

Model Diagnostics: Outliers, Leverage, and Influential Observations

Explores outliers, leverage, and influential observations in statistical models, including methods for detection and assessment.