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-BARSIC- [3]
2 years ago
15

Given: m∠ACB = m∠ABCProve: ∠XCA = ∠YBA

Mathematics
1 answer:
klio [65]2 years ago
4 0

Answer:

Given that <ACB is congruent to <ABC we can infer that the triangle formed is an isosceles triangle. If B and C are both congruent and the distance from c and x and b and y are also congruent than the angles are congruent.

Step-by-step explanation:

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Which category do both of these shapes belong to
nekit [7.7K]

Answer:

D. Parallelogram

Step-by-step explanation:

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3 years ago
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How many sides does the polygon have?<br> 8<br> 10<br> 12<br> 15
Lynna [10]

Answer:

B

Step-by-step explanation:

8 0
3 years ago
Which term best describes a figure formed by three segments connecting
elixir [45]

Answer:

The term that best describes a figure formed by three segments connecting three non-collinear points is:

Triangle.

Step-by-step explanation:

Step-by-step explanation:

We know that with the help of just one point we can't form any figure.

With the help of two points a line segment can be formed.

And with the help of three points if the three points are collinear a triangle can be formed.

Hence, the term that best describes a figure formed by three segments connecting three non-collinear points is:

Triangle.

8 0
3 years ago
PLEASE HELP <br><br> QUESTION IN PHOTO
Dmitry_Shevchenko [17]

Answer:it hink the answer is 10 cause you can add 8+2 i think

Step-by-step explanation:

8 0
3 years ago
Let U1, ..., Un be i.i.d. Unif(0, 1), and X = max(U1, ..., Un). What is the PDF of X? What is EX? Hint: Find the CDF of X first,
Kryger [21]

Answer:

E(X)= n \int_{0}^1 x^n dx = n [\frac{1}{n+1}- \frac{0}{n+1}]=\frac{n}{n+1}

Step-by-step explanation:

A uniform distribution, "sometimes also known as a rectangular distribution, is a distribution that has constant probability".

We need to take in count that our random variable just take values between 0 and 1 since is uniform distribution (0,1). The maximum of the finite set of elements in (0,1) needs to be present in (0,1).

If we select a value x \in (0,1) we want this:

max(U_1, ....,U_n) \leq x

And we can express this like that:

u_i \leq x for each possible i

We assume that the random variable u_i are independent and P)U_i \leq x) =x from the definition of an uniform random variable between 0 and 1. So we can find the cumulative distribution like this:

P(X \leq x) = P(U_1 \leq 1, ...., U_n \leq x) \prod P(U_i \leq x) =\prod x = x^n

And then cumulative distribution would be expressed like this:

0, x \leq 0

x^n, x \in (0,1)

1, x \geq 1

For each value x\in (0,1) we can find the dendity function like this:

f_X (x) = \frac{d}{dx} F_X (x) = nx^{n-1}

So then we have the pdf defined, and given by:

f_X (x) = n x^{n-1} , x \in (0,1)  and 0 for other case

And now we can find the expected value for the random variable X like this:

E(X) =\int_{0}^1 s f_X (x) dx = \int_{0}^1 x n x^{n-1}

E(X)= n \int_{0}^1 x^n dx = n [\frac{1}{n+1}- \frac{0}{n+1}]=\frac{n}{n+1}

6 0
3 years ago
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