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In this tutorial, we will explore the determinants of a good regression – the sum of squares total, the sum of squares regression and the sum of squares error. We dig deep into the concepts to define their role within the regression model and look into how they are connected.
The sum of squares total (SST) is a measure of the total variability of the dataset, while the sum of squares regression (SSR) describes how well your line fits the data. The sum of squares error (SSE), on the other hand, is the difference between the observed value and the predicted value. And what is the connection between these three concepts? Watch the whole video to find out!
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