how to calculate simple linear regression equation

Linear relationship between dependent and independent variable.Random error (ε-Epsilon) – The difference between an observed value of y and the mean value of y for a given value of x.The mathematical equation of the line is y=a+bx+ε The first step of linear regression is to test the linearity assumption, this can be performed by plot the values in a graph known as scatter plot, to observe the relationship between dependent and independent variable, because if the data is exponentially scattered then there is no meaning to create the regression equation.ĭraw the line which covers the majority of the points, further this line considered as the “best fit” line In fact, the basic difference between simple and multiple regression is in terms of explanatory variables.įor example compare the crop yield rate against the rain fall rate in a season. Whereas, in multiple linear regression more than one independent variables used to predict a single dependent variable.

In simple linear regression, there is only one independent variable used to predict a single dependent variable. Variable or Predictor variable – The variable used to explain the dependent Variable or Criterion variable – is the variable for which we wish to make a In other words, predict the change in the dependent variable according to the change in the independent variable.

Linear regression is a statistical technique to estimate the mathematical relationship between a dependent variable (usually denoted as Y) and an independent variable (usually denoted as X).

How to calculate simple linear regression equation