Answer:
Function
Step-by-step explanation:
The reason it is a function is because its saying y=x. Then you just plug in the number(s) to get the function y=4.PLEASE MARK BRAINLIEST thank you!!!!!!!!!
<u>Answer:</u>

<u>Step-by-step explanation:</u>
32a^3 + 12a^2
To factorize this, start by taking the common variable out. As we have two powers for the same variable a, we can take the smaller power of a as a common to get like shown below:
32a^3 + 12a^2
a^2 (32a + 12)
Now when you have taken the variable as a common, try and take out a common number from the coefficient of a as well:
a^2 (32a + 12)
4a^2 (8a + 3)
So, the fully factored form of 32a^3 + 12a^2 is 4a^2 (8a + 3).
Answer:
A
Step-by-step explanation:
Simple linear regression is a statistical method that summarizes and study relationships between two continuous quantitative variables.
One variable is regarded as the predictor, explanatory, or independent variable and the other variable is regarded as the response, outcome, or dependent variable.
Two variables can be denoted by X and Y.
Among the given options, the correct option is A. After conducting a hypothesis test to test that the slope of the regression equation is nonzero, you can conclude that your predictor variable, X, causes Y
Answer:
(4, 0)
Step-by-step explanation:
given the 2 equations
y = x - 4 → (1)
- 4x - 6y = - 16 → (2)
substitute y = x - 4 into (2)
- 4x - 6(x - 4) = - 16
- 4x - 6x + 24 = - 16
- 10x + 24 = - 16 ( subtract 24 from both sides )
- 10x = - 40 ( divide both sides by - 10 )
x = 4
substitute x = 4 into (1) for corresponding value of y
y = 4 - 4 = 0
solution is (4, 0)
Answer:
It is expected that linearization beyond age 20 will be use a function whose slope is monotonously decreasing.
Step-by-step explanation:
The linearization of the data by first order polynomials may be reasonable for the set of values of age between ages from 5 to 15 years, but it is inadequate beyond, since the fourth point, located at
, in growing at a lower slope. It is expected that function will be monotonously decreasing and we need to use models alternative to first order polynomials as either second order polynomic models or exponential models.