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Assoli18 [71]
3 years ago
6

Q:Subtract. Simplify your answer and write it as a proper fraction or as a whole or mixed number

Mathematics
2 answers:
Tpy6a [65]3 years ago
7 0
Simplified is 4/15
Hope this helped
Oksana_A [137]3 years ago
3 0

Answer:

4/15

Step-by-step explanation:

13/15 - 6/10 = 4/15

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What is the solution to the system of equations that is graphed below?
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5 - -3..... i think

Step-by-step explanation:

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A dog eats 1/2 of a cup of dog food per meal. how many meals are in a 6-cup bag of dog food
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6 / 1/2 
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There are 12 meals in a bag of dog food
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3 years ago
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What is 15267 times 16459 then divide by 7? ​
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Answer:

33297327

Step-by-step explanation:

(15267 * 15267) ÷ 7

= 15267 * 15267

= 233081289 ÷ 7

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Hope this helps! :)

7 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
No spams :))))))))))))
Alexandra [31]
A. Youre getting anyone willing to answer the survey, most of them might be in a rush so maybe they’ll decline so i think A is the safesy
3 0
2 years ago
Read 2 more answers
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