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navik [9.2K]
2 years ago
10

Find the percent change 50 is decreased to 14.

Mathematics
1 answer:
marusya05 [52]2 years ago
5 0

Answer:

<h2>50, percentage decreased by 14% (percent) of its value = 43</h2>
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The value of a particular antique is estimated to be £122000.
swat32
The answer is 134505
5 0
3 years ago
Solve the equation R-3=28/R R=
geniusboy [140]
<span>Remember that it's against our guidelines to copy content from other websites, books or any other material to which you don't own the rights to. We know you have the power to solve these problems on your own</span>
4 0
2 years ago
Solve the system by the substitution method.
Gemiola [76]

Answer:

(2,2)

Step-by-step explanation:

Given equations

x-3y=-4-------------------------------(1)

y=-3x+8.------------------------------(2)

Substitute 2 in 1. This means use the value of y in equation 1, to replace y

x-3(-3x+8)=-4------------------open brackets

x+9x-24=-4---------------------collect like terms

10x=-4+24

10x=20---------------------------divide by 10 both sides to get value of x

10x/10=20/10=2

x=2------------------------------use value of x in equation (2) to get value of y

y=-3x+8

y=-3(2)+8

y=-6+8=2

solutions

x=2, y=2

7 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
PLEASE HELP ME I'LL MARK BRAINLIST
postnew [5]

Answer:

(2,3)

Step-by-step explanation:

3 0
2 years ago
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