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3 years ago

A sector with an area of 11/2 pie cm has radius of 3 cm what is the central angle measure of a sector in degrees

1 answer:

6 0

A= 1/360 m•pi r^2

11/2= 1/360 m• pi (3)^2

11/2= 1/360 m• pi • 9
Divide 360 by 9

11/2= 1/40 m pi

Cross multiply

440=2pi m

Divide both sides by 2pi

m= 70.02817 degrees round if necessary

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Hi so this question is super confusing! I can't even figure it out

frutty [35]

Answer:

i think it's n/2 may be even and 3n+1 is odd. They succumb to 3n if that helps.

Step-by-step explanation:

hope it helps, have a good day/night

6 0

2 years ago

Find the area of the shaded region. Round your answer to the nearest tenth. 9 m 20 9 m area aboutm? pls explain how.

Alenkinab [10]

Answer:

The area of the shaded region is 17.4 square meters.

Step-by-step explanation:

Since the shaded region in the center equals the remainders of a circle, the area of a square equals its side squared, and the area of a circle equals π times the radius squared, for To determine the area of the shaded region, the following calculations should be performed:

(9 ^ 2) - (π x (9/2) ^ 2) = X

81 - (π x 4.5 ^ 2) = X

81 - (3.141 x 4.5 ^ 2) = X

81 - 3.141 x 20.25 = X

81 - 63.6 = X

17.4 = X

Thus, the area of the shaded region is 17.4 square meters.

5 0

3 years ago

A. (0,5)<br> B. (0,2)<br> C. (-1,3)<br> D. (-3,-1)

wolverine [178]

Answer:

(0,5)

x,y

Step-by-step explanation:

7 0

2 years ago

Let X1 and X2 be independent random variables with mean μand variance σ².

My name is Ann [436]

Answer:

a) $E(\hat \theta_1) =\frac{1}{2} [E(X_1) +E(X_2)]= \frac{1}{2} [\mu + \mu] = \mu$

So then we conclude that $\hat \theta_1$ is an unbiased estimator of $\mu$

$E(\hat \theta_2) =\frac{1}{4} [E(X_1) +3E(X_2)]= \frac{1}{4} [\mu + 3\mu] = \mu$

So then we conclude that $\hat \theta_2$ is an unbiased estimator of $\mu$

b) $Var(\hat \theta_1) =\frac{1}{4} [\sigma^2 + \sigma^2 ] =\frac{\sigma^2}{2}$

$Var(\hat \theta_2) =\frac{1}{16} [\sigma^2 + 9\sigma^2 ] =\frac{5\sigma^2}{8}$

Step-by-step explanation:

For this case we know that we have two random variables:

$X_1 , X_2$ both with mean $\mu = \mu$ and variance $\sigma^2$

And we define the following estimators:

$\hat \theta_1 = \frac{X_1 + X_2}{2}$

$\hat \theta_2 = \frac{X_1 + 3X_2}{4}$

Part a

In order to see if both estimators are unbiased we need to proof if the expected value of the estimators are equal to the real value of the parameter:

$E(\hat \theta_i) = \mu , i = 1,2$