Bivariate Data Analysis: Correlation and Regression

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Page 3 of 4

Explores multilinear regression, variance, correlation, optimization, ANOVA, and design procedures.

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

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

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

Explains the least-squares regression line, correlation coefficients, outliers, influential points, and residuals in regression models.

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

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

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Explores non-linear regression models, likelihood estimation, model fitting, and confidence intervals.

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