(untitled) (European Journal of Formal Sciences and Engineering, 2022)
This article contains the OLS method, WLS method and bootstrap methods to estimate coefficients of linear regression and their standard deviation. If regression holds random errors with constant variance and if those errors are independent normally distributed we can use least squares method, which is accurate for drawing inferences with these assumptions. If the errors are heteroscedastic, meaning that their variance depends from explanatory variable, or have different weights, we can’t use least squares method because this method cannot be safe for accurate results. If we know weights for each error, we can use weight least squares method. In this article we have also described bootstrap methods to evaluate regression parameters. The bootstrap methods improved quantile estimation. We simulated errors with non constant variances in a linear regression using R program and comparison results. Using this software we have found confidence interval, estimated coefficients, plots and results for any case.