What is the solution to the system of linear equations?

Gary has 32 ounces of soda. He shares it with 3 of his friends., splitting the soda evenly into 4 cups. He drinks 8 ounces of so

Norma-Jean [14]

0 ounces, when you split between four people there are 8 Oz in each cup, therefore he drank the whole cup.

8 0

4 years ago

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In abc, AB=9.8, AC=12.7, and BC=21.1 what is m angle A?

Alex17521 [72]

Answer:

Step-by-step explanation:

3 0

3 years ago

Mariana gets $15 per hour working at McDonald's and Jayvon gets $15 per hour as well. Jayvon had $50 in the

zhannawk [14.2K]

Answer:

What r the options?

Step-by-step explanation:

3 0

3 years ago

PLEASE HELP I'M SORRY FOR A LOT OF QUESTIONS but I need help

deff fn [24]

Answer:

Hello! answer: 268 square meters

Step-by-step explanation:

To find the total surface area you find the area of each face then add them up so...

10 × 3 = 30

8 × 3 = 24

10 × 8 = 80

Now that I found the area for each face I will just add them up so...

30 + 30 + 24 + 24 + 80 + 80 = 268 therefore the area is 268 square meters Hope that helps!

5 0

3 years ago

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From past experience, a company has found that in carton of transistors: 92% contain no defective transistors 3% contain one def

jasenka [17]

Answer:

$E(X) = 0*0.92 + 1*0.03 +2*0.03 +3*0.02 = 0.1500$

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

$E(X^2) = \sum_{i=1}^n X^2_i P(X_i)$

And replacing we got:

$E(X^2) = 0^2*0.92 + 1^2*0.03 +2^2*0.03 +3^2*0.02 = 0.3300$

The variance is calculated with this formula:

$Var(X) = E(X^2) -[E(X)]^2 = 0.33 -(0.15)^2 = 0.3075$

And the standard deviation is just the square root of the variance and we got:

$Sd(X) = \sqrt{0.3075}= 0.5545$

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:

$E(X) = \sum_{i=1}^n X_i P(X_i)$

And replacing we got:

$E(X) = 0*0.92 + 1*0.03 +2*0.03 +3*0.02 = 0.1500$

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

$E(X^2) = \sum_{i=1}^n X^2_i P(X_i)$

And replacing we got:

$E(X^2) = 0^2*0.92 + 1^2*0.03 +2^2*0.03 +3^2*0.02 = 0.3300$

The variance is calculated with this formula:

$Var(X) = E(X^2) -[E(X)]^2 = 0.33 -(0.15)^2 = 0.3075$

And the standard deviation is just the square root of the variance and we got:

$Sd(X) = \sqrt{0.3075}= 0.5545$

8 0

4 years ago