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Dummy Variables in Regression Analysis

(12 customer reviews)


Last updated on March 20, 2021 19:08


What you’ll learn

  • Describe qualitative information appropriately through dummy variables
  • Incorporating dummy variables into multiple regression models
  • Key considerations when using dummy variables

Regression analysis is a flexible tool that can be adapted to suit different types of data. Using categorical variables as predictors increases the usefulness of regression models because we are often interested in addressing questions involving group differences. For instance, a marketing student may be interested in explaining how differences in race, gender, or region may affect customer attitude or behavior.

Unlike quantitative variables, the incorporation of qualitative explanatory variables in regression models requires a special type of variables known as dummy variables and a particular technique must be followed to quantitively represent the information appropriately.

In this course, you will learn how to use qualitative information in regression models through the application of dummy variables.

We will start by discussing the issues of using categorical variables in regression analysis. Then, definition and creation of dummy variables will be explained. Next, we will learn how to use dummy variables in regression models with real dataset. The final section of this course highlights some important considerations when using dummy variables such as dummy variables trap, interpreting logarithmic dependent variables, and the correct way to choose the reference group.

On completion of this course, you will be very confident in incorporating and interpreting dummy variables in multiple regression models.

Who this course is for:

  • Learners with very basic knowledge on Regression Analysis
  • Business students enrolled in introductory econometrics and statistics courses

Course content

  • Introduction
  • The Basics
  • Categorical Variables in Regression Models
  • Important Considerations
  • Conclusion

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