1= $13.50
2= $27
3=$40.50
I Don't Know How The Hour You Looking For?
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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(0, 5) is the minimum value.
Find the axis of symmetry by plugging the respective variables into -b/2a
-5/2(0) = 0
There is no b-value in our equation, or rather, the value of b is 0. To see this, y = 2x^2 + 5 can be written as
y = 2x^2 + 0x + 5
We plug 0 into f(x), establishing every x-value as 0.
f(0) = 2(0)^2 + 5
f(0) = 0 + 5
f(0) = 5
5 is now your vertex’s y-value. Plot the two values together.
(0, 5)
We know that this is a minimum because the leading coefficient is positive, meaning the the graph’s parabola will open down.
Answer:
i think its square a
Step-by-step explanation:
<span>Y = 360,000 - 1500 X
X-intercepts; Y = 0
1500 X = 360,000
X = 240
Y-intercepts; X = 0
Y = 360,000
The Y-intercepts corresponds to the value of the property 0 months after purchase.
The X -intercepts corresponds to the number of months that have passed before the value is 0.
The property is fully depreciated after 240 months or 20 years</span>
