Regression analysis is a mathematical method for figuring out how a dependent variable (Y) is related to one or more independent factors (X) by looking at the data. The main goal of regression analysis is to predict the values of the dependent variable based on the values of one or more independent factors.
Simple linear regression and multiple linear regression are the two main types of regression analysis.
This type of regression analysis is used when there is only one variable that can be changed on its own. A straight line shows the link between the variable that is being measured and the other variable. The line is described by the equation Y = a + bX, where Y is the dependent variable, X is the independent variable, an is the line’s starting point, and b is its slope.
Multiple Linear Regression: When there are two or more independent factors, this type of regression analysis is used. A plane or a hyperplane shows the link between the dependent variable and the other factors. The equation for the plane or hyperplane is Y = a + b1X1 + b2X2 +… + bnXn, where Y is the dependent variable, X1, X2,…, Xn are the independent variables, an is the slope, and b1, b2,…, bn are the coefficients of the independent variables.
Regression analysis can be used in many areas, like banking, economics, marketing, and engineering. It can be used to predict what will happen in the future based on what has happened in the past, find trends, and find out how one thing affects another. But it’s important to remember that association doesn’t mean causation, and regression analysis should be used carefully because of its assumptions and limits.
