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:
16 cm2
Step-by-step explanation:
By equa tion of area of triangle: S = 1/2 * height * base
Triangle B: 32 = 1/2 * 8 * x --> x = 8 (cm)
The base of triangle A is half of the base of triangle B so it is 4 cm.
The area of triangle A = 1/2 * 8 * 4 = 16 (cm2)
Some fractions that are equivalent to 2/3 are 4/6 and 6/9
to find equivalent fractions, you just have to reduce the fraction or multiply both the top and bottom numbers by the same number to increase the fraction.
2/3 is as reduced as it can be, though.
The answer is B
Cause they all have the same angle points
