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Systematic Risk Decoded

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The Use of Dummy Variables in Regression Analysis
By Smita Skrivanek, Principal Statistician, MoreSteam.com LLC

What is a Dummy variable?
A Dummy variable or Indicator Variable is an artificial variable created to represent an attribute with two or more distinct categories/levels.

Why is it used?
Regression analysis treats all independent (X) variables in the analysis as numerical. Numerical variables are interval or ratio scale variables whose values are directly comparable, e.g. ‘10 is twice as much as 5’, or
‘3 minus 1 equals 2’. Often, however, you might want to include an attribute or nominal scale variable such as ‘Product Brand’ or ‘Type of Defect’ in your study. Say you have three types of defects, numbered ‘1’, ‘2’ and ‘3’. In this case, ‘3 minus 1’ doesn’t mean anything… you can’t subtracting defect 1 from defect 3. The numbers here are used to indicate or identify the levels of ‘Defect Type’ and do not have intrinsic meaning of their own. Dummy variables are created in this situation to ‘trick’ the regression algorithm into correctly analyzing attribute variables.

How is a dummy variable created?
We will illustrate this with an example: Let’s say you want to find out whether the location of a house in the
East, Southeast or Northwest side of a development and whether the house was built before or after 1990 affects its sale price. The image below shows a portion of the Sale Price dataset:

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SalePrice is the numerical response variable.

The dummy variable Y1990 represents the binary

independent variable ‘Before/After 1990’. Thus, it takes two values: ‘1’ if a house was built after 1990 and ‘0’ if it was built before 1990. Thus, a single dummy variable is needed to represent a variable with two levels.
Notice, there are only two dummy variables left, East (E) and SouthEast (SE). Together, they

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