Linear Regression Model

Definition

A linear regression model is a statistical approach used to model and analyze relationships between two variables, where one variable (dependent variable) can be predicted based on another variable (independent variable). It assumes that there exists a linear relationship between these variables.

Related terms

Slope: In linear regression, slope refers to how steep or flat the line connecting data points is. It represents the change in dependent variable for each unit increase in independent variable.

Residuals: Residuals are differences between observed values and predicted values obtained from a linear regression model. They indicate how well or poorly our model fits the data points.

Coefficient of Determination (R-squared): R-squared measures the proportion of variation in the dependent variable that can be explained by the independent variable(s). It ranges from 0 to 1, where higher values indicate a better fit.