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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8 glasses in order to make money because if you sell each glass for $2 times that by 8 it’s $16
Hello,
Answer:
The last graph (2;3)
Step-by-step explanation:
2x + 3y = 12
2x – 3y = 0
2x + 3y = 12
2x = 3y
3y + 3y = 12 ⇔ 6y = 12 ⇔ y = 12/6 = 2
2x + 3 × 2 = 12 ⇔ 2x + 6 = 12 ⇔ 2x = 6 ⇔ x = 6/2 = 3
When you simplify the expression you get 38. Hope this helps.
Answer = 38.
88(3.7+4.3)-2*2.6
88(8)-2*2.6
704-2*2.6
704-5.2
698.8
I hope it helps!
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