Basics of Linear Regression

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Related lectures (32)

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Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Applied Biostatistics: Bivariate Data and Regression Analysis

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

Linear Regression: Ozone Data Analysis

Explores linear regression analysis of ozone data using statistical models.

Comparison Statistics: Hypothesis Testing and ANOVA

Explores comparison statistics, hypothesis testing, ANOVA, correlation, and linear regression.

Regression: Simple and Multiple Linear

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

Linear Regression: Least Squares

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

Applied Biostatistics: Bivariate Data Analysis

Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.

Regression: Linear Models

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

Nonparametric Statistics: Bayesian Approach

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

Heteroskedasticity: Ch. 4a

Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.