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For instance, Least Squares Equation can be used to find the values of the coefficients a and b The least square method means that the overall solution minimizes the sum of squares of the errors made in the results of every single equation. This straight line is also known as best fit line. The least square method determines the position of a straight line or also called trend line and the equation of the line. In general, the dependent variables are demonstrated on y-axis, while the independent variables are demonstrated on x-axis. The method of least squares is a standard approach in regression analysis to determine the best fit line for a given data, It basically provides a visual relationship between the given data points. Response variable is continuous and also residuals are almost same throughout the regression line.Particularly there is no or little multicollinearity in the data.All variables of regression to be multivariate normal.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).