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: $15
Step-by-step explanation:
To find the markup price just multiply 30% by the amount.
30% * 50
0.3 * 50 = 15
You have to break up 384 into numbers that can be taken out to the radical.
You can break up 384 into 2^3*2^3*6.
Since two 2’s can be taken you would have 4 on the outside and a 6 and x^4 left on the inside.
x^3 can be taken out, leaving an x inside the radical.
The final answer would be 4x on the outside and 6x left under the cubed radical
Because on number two there is a right angle and a part of it is 66 you subtract 66 from 90 and then divide that by 31 and subtract 7. Hope that helped a little
Answer: (A) and (A)
<u>Step-by-step explanation:</u>
