Linear Regression: Pearson Correlation

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

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

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

Covers the analysis of various data attributes and linear regression models.

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

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

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