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:
0.06164
Step-by-step explanation:
The effective annual rate obtained by compounding nominal annual rate r monthly is ...
eff rate = (1 +r/12)^12 -1
Then the value of r is ...
r = 12×((eff rate) +1)^(1/12) -1)
For the given effective rate, that is ...
r = 12×(1.06341^(1/12) -1) ≈ 0.06164 . . . . nominal annual interest rate
Answer: 0.04 meters
Step-by-step explanation:
Convert cm to meters by dividing the length by 100.
4/100= = 0.04
Answer:
190.1, 101.89, 101.9, 100.789, 112, 1
Step-by-step explanation:
