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ELEN [110]
4 years ago
12

What two functions does the brain control and when we sleep

Mathematics
1 answer:
DENIUS [597]4 years ago
8 0

Answer:

circadian rhythm and homeostasis–work together to regulate when you are awake and sleep

Step-by-step explanation:

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Scores on the GRE (Graduate Record Examination) are normally distributed with a mean of 513 and a standard deviation of 82. Use
Trava [24]

Answer:

Note that we need calculate the scores between the mean (\mu=513) and the mean plus 3 times the standard deviation. It is 3 times since the distance between the values given (513 and 759) divided by the standard deviation is 3 (\frac{759-513}{82}=3)

So the rule say that 99.7% of data is between \mu - 3\sigma and \mu + 3\sigma, as it is a normal distribution half of 99.7% is between \mu and \mu+3\sigma. Hence 49.85% of people score between 513 and 759.

4 0
4 years ago
Can someone help me with all off theses plsss
Usimov [2.4K]

Answer:

3% = 0.03

30% = 0.3

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8 0
3 years ago
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Adam wanted to play his video games. His mother told him that he needed to finish his homework, clean out the dishwasher, and cl
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Step-by-step explanation:

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5 0
3 years ago
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Step-by-step explanation:

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3 years ago
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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
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