Regression analysis is a statistical method used to examine and model the relationship between one dependent variable and one or more independent variables. It helps to understand how changes in the independent variables influence the dependent variable and is widely used for prediction, forecasting, and decision-making.
There are two main types of regression:
- Simple Regression: Examines the relationship between one independent variable and one dependent variable.
- Multiple Regression: Explores relationships involving two or more independent variables.
The primary output of regression analysis is a mathematical equation, often written as:
Y = a + bX + ε,
where:
- Y: Dependent variable (e.g., sales, revenue, or outcome).
- X: Independent variable(s) (e.g., ad spend, time, or input).
- a: Intercept (the value of Y when X is zero).
- b: Coefficient (shows the impact of a unit change in X on Y).
- ε: Error term (unexplained variability).
Applications
- Business: Predict sales based on marketing spend.
- Economics: Study the effect of GDP on employment.
- Healthcare: Assess the impact of diet on blood pressure.
Regression is powerful for making informed decisions and understanding trends by quantifying relationships between variables.
