To do the best fit of line intercept, we need to apply a linear regression model to reduce the SSE value at minimum as possible. To identify a slope intercept, we use the equation. Y = mx + b, ‘m’ is the slope ‘x’ → independent variables ‘b’ is intercept; We will use Ordinary Least Squares method to find the best line intercept (b.
The least squares pri n ciple states that the SRF should be constructed (with the constant and slope values) so that the sum of the squared distance between the observed values of your dependent variable and the values estimated from your SRF is minimized (the smallest possible value).Although sometimes alternative methods to OLS are necessary, in most situations, OLS remains the most popular technique for estimating regressions for the following three reasons:.Using OLS is easier than the alternatives. Other techniques, including generalized method of moments (GMM) and maximum likelihood (ML) estimation, can be used to estimate regression functions, but they require more mathematical sophistication and more computing power. These days you’ll probably always have all the computing power you need, but historically it did limit the popularity of other techniques relative to OLS.OLS is sensible. By using squared residuals, you can avoid positive and negative residuals canceling each other out and find a regression line that’s as close as possible to the observed data points.OLS results have desirable characteristics. A desirable attribute of any estimator is for it to be a good predictor. When you use OLS, the following helpful numerical properties are associated with the results.