Understanding Dummy Variables in Regression Analysis

What are nominal variables?

• Variables with only one category

• Variables with different categories

• Variables that are always dependent

• Variables with numerical values

How can we code a nominal variable with two categories?

• By assigning random numbers

• By using colors

• By assigning binary values like 0 and 1

• By using letters

In the example of gender, what does the regression coefficient B1 represent?

• The product of male and female

• The average of male and female

• The difference between males and females

• The sum of male and female

What is the purpose of dummy variables?

• To simplify numerical variables

• To eliminate variables

• To create new categories

• To handle variables with more than two categories

How many dummy variables are needed for a variable with three categories?

• Three

• One

• Two

• Four

Why do we create fewer dummy variables than the number of categories?

• To avoid overdetermination in the regression model

• To reduce the number of equations

• To save memory

• To make calculations easier

What happens if all dummy variables are included in the regression model?

• The model becomes underdetermined

• The model becomes overdetermined

• The model becomes more accurate

• The model becomes invalid

What is the role of a reference category in dummy variables?

• It is the category used for comparison

• It is the category with the lowest value

• It is the category that is ignored

• It is the category with the highest value

How does software like data tab assist with dummy variables?

• It automatically creates dummy variables

• It eliminates the need for dummy variables

• It suggests new categories

• It calculates the regression coefficient

What should be considered when selecting a reference category?

• The category that is most common

• The category with the most data

• The category that is least common

• The category that provides meaningful comparison