Answer:$570
Step-by-step explanation:If it is 900 for 2 walls, and they need 6 walls, then multiply 900 by 3. They would use 2700 bricks for 6 walls. Multiply 2700 by 0.10 to get the price for all the bricks, 270 dollars. If labor to build each wall costs 50 dollars then multiply that by 6 and add it to the 270 dollars. It costs 570 dollars to build 6 walls
1. your leading coefficient has to be 1 (nothing before the x^2). If there is you have to divide that out before you start.
2. Move your constant (the number without any x attached) to the other side of the equation
3. take 1/2 of the b term (the one with the x attached) and then square it and then add it to both sides
4. Factor the left side
5. Set each factor equal to 0 and solve
Here is an example:
4x^2-24x+20=0
The first term is not a 1 so we have to divide it out by 4 first
x^2-6x+5=0
Move the 5 to the other side. It becomes negative.
x^2-6x=-5
Take 1/2 of 6 (3) then square it (9) and add it to both sides.
x^2-6x+9=-5+9
Factor the left side
(x-3)(x-3)=4
(x-3)^2=4
To solve you need to square root both sides
x-3=+/-
x-3=+/-2
x=3+2=5
x=3-2=1
Those would be your two answers.
<span>Hope that helps</span>
Answer: 1 1/3 packs of treats
Step-by-step explanation:
2 1/9 + 5/9 = 2 6/9, which can be simplified to 2 2/3.
We subtract 2 2/3 from 4, giving us 1 1/3
It is rotated 90 degress clockwise then reflected across the x-axis
Suppose you performed a regression analysis. The mse for this scenario is 0.105
Regression is a statistical method used in finance, making an investment, and different disciplines that attempt to determine the electricity and man or woman of the relationship between one established variable (commonly denoted through Y) and a sequence of different variables (called independent variables).
We are able to say that age and peak can be described through the usage of a linear regression version. because someone's peak will increase as age will increase, they have got a linear courting. Regression fashions are commonly used as statistical proof of claims regarding regular statistics.
"Regression" comes from "regress" which in turn comes from Latin "regresses" - to head returned (to something). In that feel, regression is the approach that permits "to head again" from messy, hard-to-interpret data, to a clearer and more significant version.
y ypred (y-ypred)^2
1 1.1 0.01
1.5 1.3 0.04
2.8 3.2 0.16
3.7 3.7 0
The error sum of the square is given by
ESS = (y- )
ESS=0.21
The mean square error is given by
ESS MSE = ESS/dfe
MSE = \frac{0.21}{2}
MSE = 0.105
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