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In this course, you will :
- introduce you to some modelling background theory and terminology, specifically the general modelling framework, the difference between modelling for explanation and modelling for prediction, and the modelling problem
- Cover basic linear regression, in which you will keep things simple by modelling the outcome variable y as a function of a single explanatory/predictor variable x.
- Basic regression using a single numerical or categorical predictor was learned.
- How do we decide which models to use? Model assessment measures quantify how well an explanatory model "fits" a set of data or how accurate a predictive model is.
- Learn about the criteria used to determine which models are the "best."
Syllabus :
- Introduction to Modeling
- Modeling with Basic Regression
- Modeling with Multiple Regression
- Model Assessment and Selection
