Linear regression is a valuable statistical method for modeling the relationship between a dependent variable and one or more independent variables.
Dependent variable (y): The variable you want to predict or explain. This is the outcome or response variable.
Independent variable (x): One or more variables you believe affect the dependent variable. They are also called predictor or explanatory variables.
Slope (m): The slope of a regression line represents the change in the independent variable (y) with a unit change in the independent variable (x). It shows the strength and direction of the relationship.
Y-intercept (b): The y-intercept is the value of the dependent variable (y) when the independent variable (x) is zero. This provides the starting point for the regression line.
Linear regression models are widely used in various fields for predictive and explanatory purposes. By fitting a linear regression model to your data, you can estimate the effect of independent variables on the dependent variable, make predictions, and gain insight into the relationships in your data set.