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:
total amount of tickets for the rides: 21+32+31=84
total amount of tickets she has: 23-6= 17
total amount of tickets she needs left: 84-17=67
answer: 67
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Step-by-step explanation:
c
if you divide the answer althrough by 2 you should get the above equation
Answer: 31.8 degrees
Step-by-step explanation:
Answer: The parabola is wider than the parent function
<u>Step-by-step explanation:</u>
If "a" is a fraction less than one, then the parabola becomes wider.
See graph (attached)
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