Linear Regression Modelling Techniques

The linear regression is a method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Building a regression model with stats packages has become straightforward. However, interpreting the software output and building a good model are no simple tasks. Learn about statistical modeling with a focus on linear models. What is a model? Estimating and interpreting model coefficients. Dealing with continuous and categorical predictors and interactions. Evaluating model performance: explanatory vs. predictive. Common pitfalls and best practices.

Regression Modelling Techniques for Categorical Data

Linear regression is inappropriate to model binary responses such as pass/fail, survived/died. Learn the principle of logistic regression part of the Generalized Linear Models along with its similarities with linear regression and its specific tools. Good practices for model-building and for assessing model goodness-of-fit are presented.