Linear Regression Model

Related lectures (32)

Page 2 of 4

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

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

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

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

Covers the probabilistic model for linear regression and the importance of regularization techniques.

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

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

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

Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.

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