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Setler [38]
3 years ago
5

The perimeter of Stephanie’s triangle is half the perimeter of Juan’s triangle. Juan’s triangle is shown. Write a numerical expr

ession to describe the perimeter of Stephanie’s triangle.

Mathematics
2 answers:
Minchanka [31]3 years ago
7 0
P=1/2(5+8+12)
Perimeter means add up all the sides. Since hers is half, multiply half by Juan’s perimeter.
denpristay [2]3 years ago
4 0

The perimeter of a triangle is the sum of all side lengths of the triangle. The numerical expression for the perimeter of Stephanie's triangle is: \frac 12 \times 25

Let the sides of Juan's triangle be x, y and z. So:

x = 5 \\ y = 8\\ z = 12

The perimeter (J) of Juan's triangle is calculated by adding all sides.

So:

J =x +y +z

This gives:

J =5 + 8 + 12

J =25

From the question, we understand that:

The perimeter (S) of Stephanie's triangle is half that of Juan.

This means that:

S = \frac 12J

Substitute 25 for J

S = \frac 12 \times 25

Hence, the numerical expression for the perimeter of Stephanie's triangle is: \frac 12 \times 25

Read more about perimeters at:

brainly.com/question/11957651

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Answer:

E(X) = \sum_{i=1}^n X_i P(X_i) = 3*0.07 +4*0.4 +5*0.25 +6*0.28= 4.74In order to find the variance we need to calculate first the second moment given by:

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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

For this case we have the following distribution given:

X          3      4       5        6

P(X)   0.07  0.4  0.25  0.28

We can calculate the mean with the following formula:

E(X) = \sum_{i=1}^n X_i P(X_i) = 3*0.07 +4*0.4 +5*0.25 +6*0.28= 4.74

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

E(X^2) = \sum_{i=1}^n X^2_i P(X_i) = 3^2*0.07 +4^2*0.4 +5^2*0.25 +6^2*0.28= 23.36

And the variance is given by:

Var(X) = E(X^2) +[E(X)]^2 = 23.36 -[4.74]^2 = 0.8924

And the deviation would be:

Sd(X) =\sqrt{0.8924} =0.9447

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