Minimizing the sum of the squared deviations around the line is called Least square estimation.
It is given that the sum of squares is around the line.
Least squares estimations minimize the sum of squared deviations around the estimated regression function. It is between observed data, on the one hand, and their expected values on the other. This is called least squares estimation because it gives the least value for the sum of squared errors. Finding the best estimates of the coefficients is often called “fitting” the model to the data, or sometimes “learning” or “training” the model.
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It's going to be C. 15... You can easily figure this out by using the equation 35 - 20 = x
Every degree is a touching point since each root factor is an x intercept
therefor the answer is 8th degree
The correct answer is D
Hope this helped :)
