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:

Step-by-step explanation:
<u>Equation of a Line</u>
We can find the equation of a line by using two sets of data. It can be a pair of ordered pairs, or the slope and a point, or the slope and the y-intercept, or many other combinations of appropriate data.
We are given a line

And are required to find a line perpendicular to that line. Let's find the slope of the given line. Solving for y

The coefficient of the x is the slope

The slope of the perpendicular line is the negative reciprocal of m, thus

We know the second line passes through (2,3). That is enough information to find the second equation:


Operating

Simplifying

That is the equation in slope-intercept form. Intercept: y=4
Answer:
(
7
n+
3
) (
n
+
7
)
Step-by-step explanation:
factor by grouping
Answer:
2.5
Step-by-step explanation:
5/2=2.5
half of 2 is 1 so for 1 cup of coco has 2.5 tbs of coco.
Answer:
17^5+17^4 = 17^4 * (17^1 + 1) = 17^4 * 18
Step-by-step explanation: