The sum of squares
In order to choose the "best" line to fit the data, regression models need to optimize some metric. For linear regression, this metric is called the sum of squares.
In the dashboard, try setting different values of the intercept and slope coefficients. In the plot, the solid black line has the intercept and slope you specified. The dotted blue line has the intercept and slope calculated by a linear regression on the dataset.
How does linear regression try to optimize the sum of squares metric?
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Intermediate Regression with statsmodels in Python
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