![]() Residual standard error: 296100 on 10 degrees of freedom As they say, a picture is worth a thousand words. Let's look at our data and a scatter plot to understand the relationship between the two. ![]() Through this analysis, we'll not only be able to see how strongly the two variables are correlated but also use our coefficients to predict the COGS for a given number of users. Today, our example will illustrate the simple relationship between the number of users in a system versus our Cost of Goods Sold (COGS). The residual is the orthogonal distance between the point in the dataset and the fitted line. In the OLS method, the model's accuracy is measured by the sum of squares for the residuals of each predicted point. It is also common with a simple linear regression model to utilize the Ordinary Least Squares ( OLS) method for fitting the model.
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