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Categorical Variables or Factors in Linear Regression in R (R Tutorial 5.7) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management
FAQs on Categorical Variables or Factors in Linear Regression in R (R Tutorial 5.7) Video Lecture - Mastering R Programming: For Data Science and Analytics - Database Management
| 1. What are categorical variables in linear regression? |  |
Ans. Categorical variables, also known as factors, are variables that take on a limited number of distinct values or categories. In the context of linear regression, these variables are used to represent qualitative data, such as gender, occupation, or educational level. They are often encoded as binary (0 or 1) indicator variables to be included in the regression model.
| 2. How are categorical variables represented in R for linear regression? | |
Ans. In R, categorical variables are commonly represented using factors. Factors allow us to assign labels to each category and assign numerical values to them. The factor() function in R is used to create a factor variable, where the levels parameter specifies the unique categories and the labels parameter assigns labels to those categories.
| 3. Why do we need to include categorical variables in linear regression? | |
Ans. Including categorical variables in linear regression allows us to account for the effect of qualitative information on the dependent variable. By including categorical variables, we can estimate the impact of different categories on the outcome variable, while controlling for other variables. This helps in understanding how different categories influence the regression model's predictions.
| 4. How do we interpret the coefficients of categorical variables in linear regression? | |
Ans. The coefficients of categorical variables in linear regression represent the average change in the dependent variable (outcome) associated with a particular category compared to a reference category. For binary categorical variables, the coefficient represents the difference in the average outcome between the two categories. The intercept term in the regression model represents the reference category.
| 5. Can we include categorical variables with more than two categories in linear regression? | |
Ans. Yes, we can include categorical variables with more than two categories in linear regression. To do this, we typically use a technique known as "dummy coding" or "one-hot encoding." This involves creating multiple binary indicator variables, where each variable represents one category compared to a reference category. These variables are then included in the regression model to estimate the effect of each category on the outcome variable.
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