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Deffense [45]
3 years ago
10

[7(p+5)-17] +[2(p-3)+2]

Mathematics
2 answers:
Fynjy0 [20]3 years ago
6 0
 It is  9p+14 because 7 times p is 7 plus 2 times p is 2 and 7 times 5 is 35 - 17 is 18 and 2 times -3 is -6 plus 2 is -4 so 18 minus four is 14 making your answer 9p + 14
Pavel [41]3 years ago
5 0
[7(p+5)-17] + [2(p-3)+2]

= 7p + 35 - 17 + 2p - 6 + 2

= 9p + 18 - 4

= 9p + 14

Just combine like terms
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Which is true of dependent events?
insens350 [35]

Answer:

Might be

Step-by-step explanation:

A.) The probability of all dependent events can be calculated using the OR formula

5 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
Find the slope of x 0 2 4 8 Y 0 12 24 48
Elodia [21]
Answer: 6
Explanation: calculator
4 0
3 years ago
Pls hurry it is 23 points i need help now Match the equation on the left with the correct estimate on the right. Each equation h
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It looks floppy but you said you need this fast so yeah

6 0
3 years ago
Read 2 more answers
Another question for math. ill give mad points, the question is in attachment
yulyashka [42]
For this case we have the following points:
 y = (- 2, 5)
 z = (1, 3)
 Therefore, the vector yz is given by:
 yz = z - y = (1, 3) - (-2, 5)
 yz = ((1 - (- 2)), (3-5))
 yz = ((1 + 2), -2)
 yz = (3, -2)
 Then, the magnitude is given by:
 lyzl = root ((3) ^ 2 + (-2) ^ 2)
 lyzl = root (9 + 4)
 lyzl = root (13)
 Answer:
 
yz = (3, -2)
 lyzl = root (13)
 option A
5 0
4 years ago
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