Regressors refer to independent variables or predictors in the context of regression analysis. Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal is to determine how changes in these independent variables affect the dependent variable.
In regression analysis, the dependent variable is the variable of interest that we want to predict or explain. On the other hand, regressors are the independent variables that are used to estimate or predict the value of the dependent variable. These variables are chosen based on their relevance, potential impact, and availability.
Regressors can be quantitative or categorical variables. In simple linear regression, there is only one regressor, while multiple regression involves two or more regressors. Each regressor has a coefficient associated with it, which represents the change in the dependent variable for a unit change in the regressor while holding other variables constant.
The selection and interpretation of regressors are essential in regression analysis. By analyzing the coefficients and their significance, regression allows us to understand the relationship between the dependent variable and the regressors, making it a valuable tool in various fields like economics, social sciences, and finance.
Overall, regressors are the vital independent variables used in regression analysis to understand and quantify the relationship between the dependent variable and other explanatory factors.
