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 the essentials of model-building, goodness-of-fit tools, model validation & common pitfalls.
Course Outline
The recommended duration for this course is 2 online session(s).
The most popular version of this course comprises 2 sessions described as follows:Session 1: Simple Linear Regression - SLR- General principle
- Model specification
- Estimating model coefficients using the data
- Interpretation & significance testing of model coefficients
- Goodness-of-fit & validation tools
- Issues in regression: Outliers, Influential observations, etc.
- Parcimonious modelling: Variable selection
- Multicollinearity
- Prediction (interpolation)
- Dangers of extrapolation
- Model validation