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:
240/sqrt(pi)
Step-by-step explanation:
We know that the area of a circle is $pi*r^2$, and we also know that the diameter of a circle is equal to $2r$.
Let's first make an equation for this problem.
pi*r^2=14400
Dividing both sides by pi, we get
r^2=14400/pi
Now, taking the square root of both sides gives us
r=120/sqrt(pi)
We are trying to find the diameter, which is twice the size of the radius.
Thus, we multiply the equation by two.
2r=240/sqrt(pi)
Answer:
1800in
Step-by-step explanation:
3ft=1yd
3x50=150ft
12in=1ft
12x150=1800in
I would try using a table. It might work better.
