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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6 divides into 78 which gives a quotient of 13.
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Answer:
Minimum: 3
Maximum: 13
Step-by-step explanation:
Georgia uses the equation
5 = |8 – x| to find the maximum and minimum values.
We solve the equation for x.
By the definition of the absolute value function, we must have:
We subtract 8 from both sides to get:
This simplifies to:
Therefore the minimum value is 3 and maximum is 13
432 million, 540 times .20 equals 108, subtract 108 from 540 and you get your answer
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