Regression analysis is a set of statistical methods used to estimate the relationships among variables. It allows us to understand how the typical value of the dependent variable (response variable) changes when any one of the independent variables (predictor variables) is varied, while the other independent variables are held fixed.
- Dependent Variable (Y): The outcome or the variable we are trying to predict or explain.
- Independent Variable(s) (X): The predictors or factors that influence the dependent variable.
- Regression Line: A line that best fits the data points in a scatter plot, showing the relationship between the dependent and independent variables.
- Coefficients: Values that quantify the relationship between each independent variable and the dependent variable.
- Intercept: The expected value of the dependent variable when all independent variables are zero.
- Linear Regression: Models the relationship between two variables by fitting a linear equation to the observed data.
- Multiple Regression: Extends linear regression by using multiple independent variables to predict the dependent variable.
- Logistic Regression: Used when the dependent variable is categorical (e.g., binary outcomes like success/failure).
- Polynomial Regression: Models the relationship between variables as an nth degree polynomial.