A model shows the visualize of what you need to do in order to complete the problem your asked. It can also show important steps and make it easier for you to answer the question, like length×height×width.
Answer: 2(0) + 8 does not equal 12, not a solution.
2(2) +8 = 12 yes it is a solution
2(-3) + 8 does not equal 12, not a solution
2(5) + 8 does not equal 12, not a solution.
Step-by-step explanation:
Looks like you need to plug in each y value given and multiplied by 2 and add 8
2(0) + 8 does not equal 12, not a solution.
2(2) +8 = 12 yes it is a solution
2(-3) + 8 does not equal 12, not a solution
2(5) + 8 does not equal 12, not a solution.
Answer:
What’s the question?
Step-by-step explanation:
Answer:
14 hours
Step-by-step explanation:
If you need to take 1 pill every 3.5 hours then this suggest that the effects of 1 pill lasts for 3.5 hours.
Therefore, if you take 4 pills then they will last: 4 x 3.5 = 14 hours
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
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