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
Learn more about regression here brainly.com/question/26755306
#SPJ4
Answer:
Monica = 48.50
Samuel = 24.25
Kara = 17.25
Step-by-step explanation:
First you write equations as you are reading the text:
m = 2s ("Monica earned twice as much as Samuel")
s = k+7 ("Samual earned 7 more than Kara")
m = 48.50
Next, you convert these equations to solve for s and k respectively.
So, if m=2s, then s=m/2:
s = m/2 = 24.25
Likewise, if s=k+7, then k=s-7
k = s-7 = 17.25
The difference between greatest and least is 48.50 - 17.25 = 31.25
<span>1594259.3333 if you just type it into your calculator. </span>
Answer:
12
Step-by-step explanation:
Think of it like an equation: (w = width, l = length)
5 = 2w - l
lw = 150
l = 150 / w
5 = 2w - (150/w)
5w = 2w^2 - 150
2w^2 - 5w - 150 = 0 <- you can see this is now a quadratic equation
w = (-b^2 + sqrt(b^2 - 4ac))/2a
w = 10
width = 10
