Answer:
Axis of symmetry
Step-by-step explanation:
Answer:
stick of butter
Step-by-step explanation:
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=
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:
Step-by-step explanation:
we know that
In a parallelogram opposite sides are equal and parallel
The figure FHJM is a parallelogram
so
therefore
It's a equilateral triangle with each side being equal to the radius.
So the area is
