Where X is the independent variable and it is plotted along the x-axis Linear Regression Equation is given below: This coefficient shows the strength of the association of the observed data between two variables. The range of the coefficient lies between -1 to +1.

The measure of the relationship between two variables is shown by the correlation coefficient. In such cases, the linear regression design is not beneficial to the given data. If there is no relation or linking between the variables then the scatter plot does not indicate any increasing or decreasing pattern.

In such cases, we use a scatter plot to simplify the strength of the relationship between the variables. It is not necessary that one variable is dependent on others, or one causes the other, but there is some critical relationship between the two variables. According to this, as we increase the height, the weight of the person will also increase. So, this shows a linear relationship between the height and weight of the person. The weight of the person is linearly related to their height. First, does a set of predictor variables do a good job in predicting an outcome (dependent) variable? The second thing is which variables are significant predictors of the outcome variable? In this article, we will discuss the concept of the Linear Regression Equation, formula and Properties of Linear Regression. The main idea of regression is to examine two things. Linear regression is commonly used for predictive analysis. There are two types of variable, one variable is called an independent variable, and the other is a dependent variable.

Linear regression is used to predict the relationship between two variables by applying a linear equation to observed data.

Equation for linear regression line