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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110,000 x 0.07 = 7700 x 25 = $192,500
Answer:C 5a^2 +70a +240
Step-by-step explanation:
Compound interest:

where

is the amount you start with,

is the interest rate,

is the number of times interest is compounded per year, and

is amount of time that passes.
The limit of the expression as x approaches -3 is -24
<h3>How to determine the limit of the expression?</h3>
The expression is given as:

As x approaches -3.
The limit expression becomes

Substitute -3 for x in the expression

Evaluate the expression

Hence, the limit of the expression as x approaches -3 is -24
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