×

Warning

The RokSprocket Module needs the RokSprocket Component enabled.

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.

Prerequisite

Prerequisite:

 

  • 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

      Summary

      • 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

Related Sessions

  • An applied set of modules with focus on the most widely used multivariate methods and their applications in several fields of application. Learn about the principle of the methods, the data needed, and the information they provide.

  • Learn about preference mapping techniques to explore and understand consumer preferences. Applications dealing with segmentation and the identification of niche markets are discussed. Focus on pitfalls and good practices.

  • Predictive analytics (PA) is on everyone's lips. But what is it really all about? Discover its principle, implementation, typical pitfalls and good practices. Learn about data wrangling and munging, a crucial step in predictive analytics. An overview of the most commonly used models is also presented.

  • The primary goal of this method is to discover which variables have the best ability of discriminating between two or more known groups in your data. Discrimimant analysis may also be used to build predictive analytics models.