# Introduction to Univariate Regression and Sum of Squared Errors

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Univariate regression is the process of fitting a line that passes through a set of ordered pairs . Specifically, given some data, univariate regression estimates the parameters and (the slope and -intercept) that fit the linear model . The best possible fit minimizes the sum of the squared distance between the fitted line and each data point, which is called the sum of squared errors (SSE). This Demonstration shows the procedure for minimizing the SSE. Click the OLS (ordinary least squares) box to show the optimal result.

Contributed by: Ben Dempe (August 2019)

Open content licensed under CC BY-NC-SA

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