Once influential factors are identified, the next goal consists of optimising their settings. This module covers the construction of experimental designs for optimisation. Data modelling is carried out with response surface methodology.
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.
Predictive analytics (PA) is on everyone's lips. But what is it really all about? Discover its principle, implementation, typical pitfalls and good practices. Learn about data wrangling and munging, a crucial step in predictive analytics. An overview of the most commonly used models is also presented.
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.
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.
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.