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This means from the given data we calculate the distance from each data point to the regression line, square it, and the sum of all of the squared errors together. The OLS method seeks to minimize the sum of the squared residuals. The OLS method is used to estimate β0 and β1. This is a line where y is the dependent variable we want to predict, x is the independent variable, and β0 and β1 are the coefficients that we need to estimate. (You may email me your equation or see me in class to. y(hat) 658.505X + 44773.979 t(hat) 658.505(S) + 44773.979 Interpret Question: Using regression on SPSS, calculate the linear regression equation to predict salary from years employed. The simple linear regression is a model with a single regressor (independent variable) x that has a relationship with a response (dependent or target) y that is a Write the regression equation that is given to you by SPSS, using S for the salary and t for years. This post will help you to understand how simple linear regression works step-by-step. If you are new to linear regression, read this article for getting a clear idea about the implementation of simple linear regression. This post is about the ordinary least square method (OLS) for simple linear regression. Ordinary Least Square (OLS) Method for Linear Regression