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natulia [17]
3 years ago
6

What is the area of this...

Mathematics
1 answer:
Verizon [17]3 years ago
4 0
The area of the following shape is 50
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Triangle TRS is similar to triangle TMN. Angle T = 40°, angle R = 60°, and angle S = 80° . What is the measure of angle M
Andre45 [30]

Answer:

60degrees

Step-by-step explanation:

For similar triangles, the angles of their angles are equal to matter the size.

Hence if triangle TRS is similar to triangle TMN, then;

<R = <M and <S = <N

Given that;

<T = 40 degrees

<R = 60degrees

<S = 80 degrees

Since <R = <M, then <M = 60degrees

7 0
2 years ago
8b + 4 - 4b + 2 = <br><br> A) 12b + 6 <br> B) 4b^2 ​2 ​​ + 4 + 2 <br> C) 12bb + 6 <br> D) 4b + 6
trapecia [35]
<h2>Answer:</h2><h2>D. 4b + 6</h2><h2 /><h2>Hope this helps!!</h2>

5 0
3 years ago
Read 2 more answers
Inverse function. sketch the graph of each function.​
Nikitich [7]

Answer:

I cant see picture

Step-by-step explanation:

8 0
3 years ago
Help please I really need help, thank you
nirvana33 [79]

Answer:

What do you need help with? Answer and I will tell you anything you need.

Step-by-step explanation:

6 0
3 years ago
Let X1,X2......X7 denote a random sample from a population having mean μ and variance σ. Consider the following estimators of μ:
Viefleur [7K]

Answer:

a) In order to check if an estimator is unbiased we need to check this condition:

E(\theta) = \mu

And we can find the expected value of each estimator like this:

E(\theta_1 ) = \frac{1}{7} E(X_1 +X_2 +... +X_7) = \frac{1}{7} [E(X_1) +E(X_2) +....+E(X_7)]= \frac{1}{7} 7\mu= \mu

So then we conclude that \theta_1 is unbiased.

For the second estimator we have this:

E(\theta_2) = \frac{1}{2} [2E(X_1) -E(X_3) +E(X_5)]=\frac{1}{2} [2\mu -\mu +\mu] = \frac{1}{2} [2\mu]= \mu

And then we conclude that \theta_2 is unbiaed too.

b) For this case first we need to find the variance of each estimator:

Var(\theta_1) = \frac{1}{49} (Var(X_1) +...+Var(X_7))= \frac{1}{49} (7\sigma^2) = \frac{\sigma^2}{7}

And for the second estimator we have this:

Var(\theta_2) = \frac{1}{4} (4\sigma^2 -\sigma^2 +\sigma^2)= \frac{1}{4} (4\sigma^2)= \sigma^2

And the relative efficiency is given by:

RE= \frac{Var(\theta_1)}{Var(\theta_2)}=\frac{\frac{\sigma^2}{7}}{\sigma^2}= \frac{1}{7}

Step-by-step explanation:

For this case we assume that we have a random sample given by: X_1, X_2,....,X_7 and each X_i \sim N (\mu, \sigma)

Part a

In order to check if an estimator is unbiased we need to check this condition:

E(\theta) = \mu

And we can find the expected value of each estimator like this:

E(\theta_1 ) = \frac{1}{7} E(X_1 +X_2 +... +X_7) = \frac{1}{7} [E(X_1) +E(X_2) +....+E(X_7)]= \frac{1}{7} 7\mu= \mu

So then we conclude that \theta_1 is unbiased.

For the second estimator we have this:

E(\theta_2) = \frac{1}{2} [2E(X_1) -E(X_3) +E(X_5)]=\frac{1}{2} [2\mu -\mu +\mu] = \frac{1}{2} [2\mu]= \mu

And then we conclude that \theta_2 is unbiaed too.

Part b

For this case first we need to find the variance of each estimator:

Var(\theta_1) = \frac{1}{49} (Var(X_1) +...+Var(X_7))= \frac{1}{49} (7\sigma^2) = \frac{\sigma^2}{7}

And for the second estimator we have this:

Var(\theta_2) = \frac{1}{4} (4\sigma^2 -\sigma^2 +\sigma^2)= \frac{1}{4} (4\sigma^2)= \sigma^2

And the relative efficiency is given by:

RE= \frac{Var(\theta_1)}{Var(\theta_2)}=\frac{\frac{\sigma^2}{7}}{\sigma^2}= \frac{1}{7}

5 0
3 years ago
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