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 hard to tell due to no labels, but these are your angles due to the equal angles theorem and a few other theorems. Hopefully with this image you can hopefully find your answer. You may need to add angles together depending on what you are looking for. And possible even figure out what angle measure would make a (180 degree) triangle.
Hope this helped
I think this is the answer ----> 8.7 (8.66 rounded)
Answer:
Number 1 is 10 and number 2 is 7
14/2 = 7
7 + 3 = 10
Answer:
n + f = 15
Step-by-step explanation:
n + f = 15
n + (6) = 15
n = 15 - 6 (opposite side opposite sign)
n = 9
