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Thepotemich [5.8K]
3 years ago
15

What is the equation for 5(x+2)=40

Mathematics
2 answers:
OleMash [197]3 years ago
6 0

Hey there!

5(x + 2) = 40

5(x) + 5(2) = 40

5x + 10 = 40

5x + 10 - 10 = 40 - 10

5x = 30

5x/5 = 30/5

x = 6

Answer: x = 6 ☑️

Good luck on your assignment and enjoy your day!

~Amphitrite140:)

Ugo [173]3 years ago
3 0

Answer:

Step-by-step explanation:

Here you go mate

  • Use PEMDAS
  • Parenthesis,Exponent,Multiplication,Division,Addition,Subtraction

Step 1

5(x+2)=40  Equation/Question

Step 2

5(x+2)=40  Remove parenthesis

5x+10=40

Step 3

5x+10=40  Subtract 10

5x=30

Step 4

5x=30  Divide sides by 5

Answer

x=6 ☑️

Hope this helps

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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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