Building a regression model with stats packages has become straightforward. However, interpreting the software output and building a good are no simple tasks. Learn the essentials of model-building, goodness-of-fit tools & common pitfalls.
This module offers an overview of tree-based modeling techniques. Learn how they work, when to use them, their strengths and weaknesses, and their implementation including validation. Several real-life applications are presented.
Classical linear regression is inappropriate when predictors are correlated – multicollinearity. Learn about Principal Component Regression (PCR) & Partial Least Squares (PLS) regression to deal with multicollinearity and when it is relevant to use them.
Linear regression is inappropriate to model binary responses such as pass/fail, alive/dead. Learn the principle of logistic regression, its similarities with linear regression and its specific tools. Good practices for model-building are presented.