Linear regression

Photo by Emily Campbell on Unsplash

Sum of the squares of the errors as a measure of how well our line fits the data.

Our goal is to find a line (m and b) that has minimal SSE.

The correlation coefficient (R) gives us a way to measure the reliability of our predictions.

For our data, how well do variations in the variable x correspond to variations in the variable y? That is, how do the variables co-vary.

The stronger the correlation between two variables, the more reliable prediction we get from the least squares regression line.



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