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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The number of days Ashley ate Mexican food while on vacation is 20 days.
<h3>How many days did Ashley eat mexican food?</h3>
The first step is to determine the fraction of the number of days Ashley atae Mexican food. In order to do this, divide 4 by 7
4/7.
The second step is to multiply 4/7 by 35
4/7 x 35= 20 days
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Answer:
\frac{79}{9} \pi
Step-by-step explanation:
Answer:
2 pi
Step-by-step explanation:
The radius is 2
The area of a circle is
A = pi r^2
We have 1/2 of a circle so
1/2 A = 1/2 pi r^2
=1/2 pi ( 2)^2
=1/2 pi *4
= 2 pi
