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Pepsi [2]
3 years ago
6

Solve For X 1 < x + 3 < 4

Mathematics
2 answers:
nignag [31]3 years ago
4 0

Answer:

3

Step-by-step explanation:

EleoNora [17]3 years ago
3 0

Answer:

− 2 < x < 1

Step-by-step explanation:

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The population of a certain species of oak tree in a national forest declines each year by one-fourth. So, after each year, only
dolphi86 [110]

Answer: 25%

Step-by-step explanation: The question tells us that the population decreases by 1/4 every single year. That means, since 1/4 equals .25, the population decreases by 25% every year :)

7 0
3 years ago
Find the real solutions of the equation by graphing
Vlada [557]

Answer:

C. 0, 1.29, -0.65

Step-by-step explanation:

The real solutions of the equation 19x^3-12x^2-16x=0  is where the graph of y=19x^3-12x^2-16x intersects the x-axis.

This means that we need to graph y=19x^3-12x^2-16x using a graphing software.

From the graph shown in the attachment x=0, x=1.29, and x=-0.65  are the  real solutions.

The correct choice is C

8 0
3 years ago
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Mice21 [21]

Answer:

i don't understand this and i'm in high school how is this possible????????????

Step-by-step explanation:

6 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
172+(-167)+(-10)+(-144) what's the sum
Pepsi [2]
-149 would be your answer
6 0
3 years ago
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