Linear Regression: Understanding Quantitative Relationships

Related lectures (32)

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

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

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

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

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

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

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.

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

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

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