title author Introduction to Statistical Modelling Prof. Joris Vankerschaver ## Presentations - 1 - Linear Regression (1/7): Introduction - 1 - Linear Regression (2/7): Simple linear regression - 1 - Linear Regression (3/7): Multiple linear regression - 1 - Linear Regression (4/7): Predictivity and variability - 1 - Linear Regression (5/7): Outliers - 1 - Linear Regression (6/7): Categorical variables - 1 - Linear Regression (7/7): Multicollinearity - [2 - Principal Component Analysis (1/3): Introduction](02a-pca-introduction.qmd) - [2 - Principal Component Analysis (2/3): Theory and concepts](02b-pca-theory.qmd) - [2 - Principal Component Analysis (3/3): Examples](02c-pca-applications.qmd) - [3 - Logistic regression](02d-logistic-regression.qmd) - [4 - Nonlinear regression (1/5): Parameter estimation](03a-parameter-estimation.qmd) - [4 - Nonlinear regression (2/5): Confidence intervals](03b-quality-of-estimation.qmd) - [4 - Nonlinear regression (3/5): Model selection](03c-model-selection.qmd) - 4 - Nonlinear regression (4/5): Sensitivity analysis - 4 - Nonlinear regression (5/5): Case study: Streeter-Phelps model