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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Sine squared x + cos squared x is equal to 1
Answer:
1. X = 1 - 3y
2. -5/6x + 7/3
Those are the answers to the problems
The two-dimensional figures would be;
Rectangles and Triangles
Rectangles would be front, back and bottom
Triangles would be the 2 right and left sides.
