Regression is the statistical method that helps us estimate or predict the unknown value of one variable from the known value of a related variable. It is widely used in various fields such as economics, finance, social sciences, and engineering to analyze and model the relationship between variables.

Explanation:

Regression involves finding a mathematical equation that best describes the relationship between two or more variables. It is based on the concept of a dependent variable and one or more independent variables. The dependent variable is the variable that we want to estimate or predict, while the independent variables are the known values that we use to make the estimation or prediction.

Key Points:
- Regression analysis helps us understand the relationship between variables and enables us to make predictions or estimations based on this relationship.
- It is often used to examine how changes in one variable are associated with changes in another variable.
- The most common type of regression analysis is simple linear regression, which assumes a linear relationship between the variables.
- In simple linear regression, the equation takes the form of Y = a + bX, where Y is the dependent variable, X is the independent variable, a is the intercept, and b is the slope.
- The intercept represents the value of the dependent variable when the independent variable is zero, while the slope represents the change in the dependent variable for a one-unit increase in the independent variable.
- Regression analysis can also be extended to multiple regression, where there are more than one independent variables.
- Multiple regression allows us to account for the effects of multiple variables on the dependent variable and can provide more accurate predictions.
- Regression analysis is typically performed using statistical software, which calculates the coefficients of the equation and provides measures of the goodness of fit and statistical significance.

In conclusion, regression is a powerful statistical method that allows us to estimate or predict unknown values based on known values of related variables. It is widely used in various fields to analyze and model relationships between variables, providing valuable insights and predictions.