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Anna11 [10]
3 years ago
5

Is this comparison true 5.14<5.041

Mathematics
2 answers:
AVprozaik [17]3 years ago
7 0
No it's not true because 5.14 is great her than 5.014
Assoli18 [71]3 years ago
6 0
No I believe this comparison is incorrect because when the number is in the tenths it has more value than a number in the hundredths
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Order from least to greatest 0.5 0.41 3/5
FinnZ [79.3K]
Least. 0.41, 0.5, 3/5 Greatest
4 0
3 years ago
Read 2 more answers
-1/5y+7=7<br> What is the value of y?
liq [111]
The answer is y=0

See the attached photo for details

Hope this helps! Please make me the brainliest, it’s not necessary but appreciated, I put a lot of effort and research into my answers. Have a good day, stay safe and stay healthy.

8 0
3 years ago
Can anybody help me with this plzzz‼️<br> (If answer is correct i will cashapp you $6)
Ganezh [65]

Step-by-step explanation:

h(x) = 3. g(x) + 5

x= -1 h(x) = 3×8 + 5= 29

x= 0h(x) = 3×5 + 5= 20

x= 2 h(x) = 3×1 + 5= 8

x= 5 h(x) = 3×-5 + 5= -10

4 0
3 years ago
Read 2 more answers
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
the perimeter of the quadrilateral is 49 centimeters. find the length of each side. x+9 , x^2-2x , x+4 , 3x-4
shtirl [24]
X= 5 or -8

Add all the like terms and set it equal to 49, use quadratic formula or factoring. 
3 0
3 years ago
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