The green mathematics tells about the impulse response of an in homogeneous linear differential operator.
According to the statement
we have to explain the green mathematics.
In mathematics, Actually there is a Green Function which was founded by a mathematician George Green.
In this function, a Green's function is the impulse response of an in homogeneous linear differential operator defined on a domain with specified initial conditions or boundary conditions.
The example of green function is the Green's function G is the solution of the equation LG = δ, where δ is Dirac's delta function; the solution of the initial-value problem Ly = f is the convolution (G ⁎ f), where G is the Green's function.
Actually in this function, it gives the relationship between the line integral of two dimensional vector over a closed path by a integral.
In this there is a green theorem, which relates a line integral around a simply closed plane curve C and a double integral over the region enclosed by C.
So, The green mathematics tells about the impulse response of an in homogeneous linear differential operator.
Learn more about FUNCTION here brainly.com/question/2328150
#SPJ1
Answer:
When it crosses y-axis it means that x coordinate must be 0 so let substitute 0 to x, y=5-2*0
So coordinates are [0,5]
9514 1404 393
Answer:
$400
Step-by-step explanation:
If the price after taking 25% off is $300, then $300 is 75% of the original price.
$300 = 0.75p
$300/0.75 = p = $400
The original price was $400.
_____
<em>Check</em>
0.25 × $400 = $100
$400 -100 = $300 . . . . the price after taking 25% off
Basket A is 3/16 red.
Basket B is 3/15 red.
The answer is Basket B.
Answer:
The histogram is right-skewed.
Step-by-step explanation:
The income of all households in the United States can be categorized as low, medium and high.
Not many people earn a high income. So the proportions of people decreases as the income increases.
Most of the people earn a medium income. So the mode of the data would be somewhere in the start of the the distribution.
There are many households that earn a low income. But this proportion is not more than the proportion of people earning low income.
So the histogram for income distribution will have a long right tail with maximum data at the starting point.
This implies that the histogram is right-skewed.
