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Dovator [93]
3 years ago
10

Ms.Candos has 90 invitations to hand out. She hand out 42 of them. The rest will be handed out by 6 students. Each students will

hand out an equal number of invitations. How many invitations will each student hand out.
Mathematics
1 answer:
REY [17]3 years ago
7 0

Answer: 8 invitations each

Step-by-step explanation:

Ms. Candos has 90 invitations from which she will hand out 42 by herself. The number of invitations left will be:

= 90 - 42

= 48 invitations

There are 6 students to share out the rest. They will hand out:

= 48 / 6 students

= 8 invitations each

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Luis, Diego, and Cecil are going fishing.
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Answer:

7 worms

Step-by-step explanation:

Let each can have x worms, so we can say

Luis has 4x worms

Diego has 3x + 2 worms

Cecil has 2x worms

Since, in total they have 65 worms, we can write and equation and solve:

4x + 3x + 2 + 2x = 65

9x + 2 = 65

9x = 63

x = 63/9

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Solve the equation 5x=3x^2+1
Arte-miy333 [17]

Answer:

-1

Step-by-step explanation:

Step 1: Simplify both sides of the equation.

5x=6x+1

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Step 3: Divide both sides by -1.

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4 years ago
Chris's credit card has a restriction that he must pay either 10 percent of the unpaid balance or $25, whichever is greater. Whi
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3 0
3 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
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