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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C. 84
12 x 8 is the area of the whole thing but you subtract 12 because of the missing 6x2 part of the square
Answer: 15.7659
Explanation: the column the 4 is in is the ten thousandths column. Since the number after it is 8, which is bigger than 5, you round up to make is 15.7659
Answer: 11x1.5=16.5 if im correct
Step-by-step explanation:B)
