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Linear Regression Techniques

Linear Regression 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
      • Checking model adequacy
      • 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