 ## Linear Regression Modelling Techniques

• 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.
Simple Linear Regression (SLR)
• Objectives
• Terminology
• What is a model and specification?
• Principle of least squares estimation
• Interpretation of model coefficients
• Difference between correlation and regression
• Statistical testing of model coefficients: intercept and slope
• Condition of use and diagnostic tools
• Prediction in regression analysis
• Extrapolation: Use & Piftalls
Multiple Linear Regression (MLR)
• Objectives
• Aspects common to SLR
• Interpretation of model coefficients
• Model-building steps
• Good and not so good measures of model performance
• Specific issues in MLR: Variable selection, multicollinearity and use of special terms
Alternatives to Standard Linear Regression
• Nonlinear Regression (NLR)
• Applications of nonlinear regression
• Overview of other ways to handle multicollinearity
Summary
• Steps in model construction
• Robustness of regression to deviations from conditions of application
• Participants should know the essential tools in statistics - descriptive statistics, both numerical and graphical, as well as hypothesis testing and confidence intervals.
• Potential participants should either have attended the training session Fundamental Tools in Statistics or should possess a similar background.
Upon completion of this module, participants will be able to:
• Understand the context of use of simple and multiple linear regression
• Construct simple/multiple regression models
• Assess the goodness-of-fit of the model to the data
• Identify common issues in regression, diagnose problems and fix them
• Interpret statistical software output

Recommended Duration: 1 day(s)

Course Materials:

TBD

Case Studies:

N/A