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2 years ago

Can some one go to my page and answer my other question? it is

Mathematics

2 answers:

Kryger [21]2 years ago

4 0

Sure i’ll go see it!

Dmitry_Shevchenko [17]2 years ago

3 0

Answer:

ill try

Step-by-step explanation:

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Pls help me what is the equation for this graph pls get it right :)

rewona [7]

Answer: Y= 1/8x -2

Step-by-step explanation:

Y = mx+ b

Slope = y2-y1/x2-x1 = -1+2 / 8-0 = 1/8

Initial Y-Value = -2

5 0

2 years ago

Read 2 more answers

What is the square root of 1,024

Shkiper50 [21]

1024 can be factored into
2(512) 
=2^2(256) 
=2^3(128) 
=2^4(64) 
=2^5(32) 
=2^6(16) 
=2^10 

Therefore, 
√1024 = √(2^10) = 2^5 = 32

4 0

2 years ago

If ABCDE is translated 7 units to the right and 2 units up, what will happen to the polygon?

Natali [406]

It will move but won't change shape or size.

6 0

2 years ago

A normally distributed random variable with mean 4.5 and standard deviation 7.6 is sampled to get two independent values, X1 and

mr Goodwill [35]

Answer:

Bias for the estimator = -0.56

Mean Square Error for the estimator = 6.6311

Step-by-step explanation:

Given - A normally distributed random variable with mean 4.5 and standard deviation 7.6 is sampled to get two independent values, X1 and X2. The mean is estimated using the formula (3X1 + 4X2)/8.

To find - Determine the bias and the mean squared error for this estimator of the mean.

Proof -

Let us denote

X be a random variable such that X ~ N(mean = 4.5, SD = 7.6)

Now,

An estimate of mean, μ is suggested as

$\mu = \frac{3X_{1} + 4X_{2} }{8}$

Now

Bias for the estimator = E(μ bar) - μ

= $E( \frac{3X_{1} + 4X_{2} }{8}) - 4.5$

= $\frac{3E(X_{1}) + 4E(X_{2})}{8} - 4.5$

= $\frac{3(4.5) + 4(4.5)}{8} - 4.5$

= $\frac{13.5 + 18}{8} - 4.5$

= $\frac{31.5}{8} - 4.5$

= 3.9375 - 4.5

= - 0.5625 ≈ -0.56

∴ we get

Bias for the estimator = -0.56

Now,

Mean Square Error for the estimator = E[(μ bar - μ)²]

= Var(μ bar) + [Bias(μ bar, μ)]²

= $Var( \frac{3X_{1} + 4X_{2} }{8}) + 0.3136$

= $\frac{1}{64} Var( {3X_{1} + 4X_{2} }) + 0.3136$

= $\frac{1}{64} ( [{3Var(X_{1}) + 4Var(X_{2})] }) + 0.3136$

= $\frac{1}{64} [{3(57.76) + 4(57.76)}] } + 0.3136$

= $\frac{1}{64} [7(57.76)}] } + 0.3136$

= $\frac{1}{64} [404.32] } + 0.3136$

= $6.3175 + 0.3136$

= 6.6311

∴ we get

Mean Square Error for the estimator = 6.6311

6 0

3 years ago

Please help with this question

goldfiish [28.3K]

Second answer box, try that

3 0

3 years ago