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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Answer: 2x(3x−1)(x+2)
Step-by-step explanation:
Answer:
A
Step-by-step explanation:
i think
Answer:
n=0
Step-by-step explanation:
Clear parenthesis w/ distribution:
3(-7n+1)=-21n+3
Move everything over to one side:
6n+3=-21n+3
6n=21n Subtract the 3 on the right.
-15n=0 Subtract the 21.
n=0 Divide the -15.
