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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 and common pitfalls.

Course Details

Learning Objectives

Learning Objectives:

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

Target Audience

Target Audience:

This module is aimed at all people who collect data and who must make decisions based on them. The regression techniques covered in this session are particularly useful to people interested in relating/predicting a variable to/from a single or a set of explanatory variables.




  • 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.


Course Outline

Course Outline:

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 and 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


      • Steps in model construction
      • Robustness of regression to deviations from conditions of application


Practical Info

Practical Info:

Recommended Duration: 1 day

Course Materials:

  • Course notes
  • Sample datasets
  • References

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