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vodomira [7]
3 years ago
8

How do you solve this ?​

Mathematics
2 answers:
Sedbober [7]3 years ago
7 0

"The equals sign with the squiggly line above it is the sign for congruence."

Therefore, GHIJ is congruent to PQRO.

H is then known as 99°;

J as 85°;

R as 123°;

P as 53°;

and all of the sides' km as listed.

If I'm understanding the question correctly, it is asking you for the appropriate degree of J, which we see as 85°.

Hope this helped!

Sav [38]3 years ago
4 0

Answer:

what the person above me said XD

Step-by-step explanation:

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Prove that if {x1x2.......xk}isany
Radda [10]

Answer:

See the proof below.

Step-by-step explanation:

What we need to proof is this: "Assuming X a vector space over a scalar field C. Let X= {x1,x2,....,xn} a set of vectors in X, where n\geq 2. If the set X is linearly dependent if and only if at least one of the vectors in X can be written as a linear combination of the other vectors"

Proof

Since we have a if and only if w need to proof the statement on the two possible ways.

If X is linearly dependent, then a vector is a linear combination

We suppose the set X= (x_1, x_2,....,x_n) is linearly dependent, so then by definition we have scalars c_1,c_2,....,c_n in C such that:

c_1 x_1 +c_2 x_2 +.....+c_n x_n =0

And not all the scalars c_1,c_2,....,c_n are equal to 0.

Since at least one constant is non zero we can assume for example that c_1 \neq 0, and we have this:

c_1 v_1 = -c_2 v_2 -c_3 v_3 -.... -c_n v_n

We can divide by c1 since we assume that c_1 \neq 0 and we have this:

v_1= -\frac{c_2}{c_1} v_2 -\frac{c_3}{c_1} v_3 - .....- \frac{c_n}{c_1} v_n

And as we can see the vector v_1 can be written a a linear combination of the remaining vectors v_2,v_3,...,v_n. We select v1 but we can select any vector and we get the same result.

If a vector is a linear combination, then X is linearly dependent

We assume on this case that X is a linear combination of the remaining vectors, as on the last part we can assume that we select v_1 and we have this:

v_1 = c_2 v_2 + c_3 v_3 +...+c_n v_n

For scalars defined c_2,c_3,...,c_n in C. So then we have this:

v_1 -c_2 v_2 -c_3 v_3 - ....-c_n v_n =0

So then we can conclude that the set X is linearly dependent.

And that complet the proof for this case.

5 0
3 years ago
Simpify the expression.<br>5+4z-2z<br><br>Simpilfied expression ​
Dafna11 [192]

Answer:

2z + 5

Step-by-step explanation:

you have to combine like terms

subtract 4z - 2z and you get 2z

the number with the variable next to it goes first not the constant

2z + 5

the variable is z and the constant is 5 because it doesn't have variable next to it

hope this makes sense and helps :)

tell me if you have any questions

4 0
3 years ago
Read 2 more answers
ASAP PLEASE HELP ME!!!!!!!!!!!!!!
mariarad [96]

Answer:

Average: 3250

Step-by-step explanation:

8 0
3 years ago
Read 2 more answers
Please help me with this question.
Amiraneli [1.4K]
Newton is 762 Km east of Mayfield. 

I send you a file wich the answer. 

4 0
3 years ago
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The probability distribution of random variable, X, is defined as follows:
stealth61 [152]

Answer and Step-by-step explanation:

A) For the model to be a probability distribution, it has to follow two conditions:

1) The probability of each value of the discrete random variable is between, and included, 0 and 1:

2) The sum of all probabilities is 1;

In the table shown, the probabilities are from 0 to 0.3 - between 0 and 1;

Adding the probabilities: 0 + 0.3 + 0.1 + 0.3 + 0.3 = 1

Therefore, this model is a valid probability distribution model.

B) They are discrete because each value correspond to a finite number of possible values.

C) Expected value is calculated by

E(X) = \Sigma xP(x)

E(X) = 0.0 + 1*0.3 + 2*0.1 + 3*0.3 + 4*0.3

E(X) = 2.6

D) P(X=3) = P(3), which means probability of 3:

P(X=3) = 0.3

E) P(X<4): probabilities of values of X that are less than 4:

P(X<4) = P(0) + P(1) + P(2) + P(3)

P(X<4) = 0 + 0.3 + 0.1 + 0.3

P(X<4) = 0.7

F) P(X>0) = P(1) + P(2) + P(3) + P(4)

P(X>0) = 0.3 + 0.1 + 0.3 + 0.3

P(X>0) = 1

G) P(X=5) = P(5)

There is no probability of P(5) because the model doesn't "cover" that number.

H) P(X=0) = P(0)

P(X=0) = 0

I) The total area of any density curve is 1 because it represents all the possible values a variable can assume.

3 0
4 years ago
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