An explanatory variable is a type of independent variable used in statistical analysis to explain changes in a dependent variable. It is also known as a predictor variable, regressor variable, or covariate. The explanatory variable is often denoted by “X” in statistical equations and models.
Explanatory variables are used to understand the relationship between two or more variables. They can be used to explain how one variable affects another variable, or to predict the value of a dependent variable based on the values of one or more independent variables.
In statistical analysis, explanatory variables are used in regression analysis, which is a technique used to estimate the relationship between a dependent variable and one or more independent variables. Regression analysis is commonly used in fields such as economics, social sciences, psychology, and engineering to understand how changes in one variable affect another variable.
For example, suppose we are interested in understanding how a person’s income (dependent variable) is affected by their education level (explanatory variable). We can collect data on a sample of individuals, where we measure their income and their education level. We can then use regression analysis to estimate the relationship between income and education level.
In this example, the education level is the explanatory variable because it is used to explain changes in the dependent variable (income). We can use regression analysis to estimate how much of the variation in income is explained by education level, and we can use this information to make predictions about the income of individuals with different education levels.
Explanatory variables can be either continuous or categorical. Continuous explanatory variables are variables that can take on any value within a range, such as age, height, or weight. Categorical explanatory variables are variables that can take on a limited set of values, such as gender, education level, or occupation.
When using explanatory variables in statistical analysis, it is important to ensure that they are independent of each other. This means that the explanatory variables should not be correlated with each other, as this can lead to problems with multicollinearity. Multicollinearity occurs when two or more explanatory variables are highly correlated, making it difficult to estimate the independent effect of each variable on the dependent variable.