Types of Variables in Regression Analysis:

- Independent Variables:
Independent variables are the input or predictor variables in regression analysis. These variables are manipulated or controlled by the researcher to understand their impact on the dependent variable. Independent variables are also known as explanatory variables as they explain changes in the dependent variable. In a regression model, independent variables are graphed on the x-axis and are used to predict the outcome of the dependent variable.

- Dependent Variables:
Dependent variables are the output or response variables in regression analysis. These variables are influenced by the independent variables and are the focus of the study. The dependent variable is what we are trying to predict or understand based on the changes in the independent variables. In a regression model, the dependent variable is graphed on the y-axis, and the relationship between the independent and dependent variables is analyzed to make predictions or draw conclusions about the data.

In regression analysis, the relationship between independent and dependent variables is examined to understand how changes in the independent variables affect the dependent variable. By analyzing these relationships, researchers can make predictions, test hypotheses, and gain insights into the factors that influence the outcome of interest.