Approximately 68% of a normal distribution lies within one standard deviation of the mean, so this corresponds to students with scores between (57.5 - 6.5, 57.5 + 6.5) = (51, 64)
H(x)=−4.9x2+21.3x?
we want to know what x is when the ball hits the ground
well when the ball hits the ground the height between the ball and the ground is 0
so you want to solve for x when h(x)=0
Answer: X/8 + X/16 = 3/4
Step-by-step explanation:
you’re just dividing the distance by the rate you are going.
Create the sum of the given polynomial terms.
f(x,y) = 3x² - 2x²y + 2xy² + 4y² + xy² - y² + 2x²y
= 3x² + 3xy² + 3y²
Test the given terms to see if they belong in f(x,y).
3: NO
3x²: YES
3y²: YES
3xy²: YES
4x²: NO
Answer:
The terms in the sum of the given polynomials are 3x², 3y², and 3xy².
Answer:

In order to find the variance we need to find first the second moment given by:

And replacing we got:

The variance is calculated with this formula:
![Var(X) = E(X^2) -[E(X)]^2 = 0.33 -(0.15)^2 = 0.3075](https://tex.z-dn.net/?f=%20Var%28X%29%20%3D%20E%28X%5E2%29%20-%5BE%28X%29%5D%5E2%20%3D%200.33%20-%280.15%29%5E2%20%3D%200.3075)
And the standard deviation is just the square root of the variance and we got:

Step-by-step explanation:
Previous concepts
The expected value of a random variable X is the n-th moment about zero of a probability density function f(x) if X is continuous, or the weighted average for a discrete probability distribution, if X is discrete.
The variance of a random variable X represent the spread of the possible values of the variable. The variance of X is written as Var(X).
Solution to the problem
LEt X the random variable who represent the number of defective transistors. For this case we have the following probability distribution for X
X 0 1 2 3
P(X) 0.92 0.03 0.03 0.02
We can calculate the expected value with the following formula:

And replacing we got:

In order to find the variance we need to find first the second moment given by:

And replacing we got:

The variance is calculated with this formula:
![Var(X) = E(X^2) -[E(X)]^2 = 0.33 -(0.15)^2 = 0.3075](https://tex.z-dn.net/?f=%20Var%28X%29%20%3D%20E%28X%5E2%29%20-%5BE%28X%29%5D%5E2%20%3D%200.33%20-%280.15%29%5E2%20%3D%200.3075)
And the standard deviation is just the square root of the variance and we got:
