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Vikentia [17]
3 years ago
6

Does any one know how to solve this -4n^2-2n=-6-7n-5n^2

Mathematics
2 answers:
den301095 [7]3 years ago
6 0
-4n^2-2n=-6-7n-5n^2 \\ -4n^2-2n+6+7n+5n^2=0\\n^2+5n+6=0 \\ n^2+2n+3n+6=0 \\ n(n+2)+3(n+2)=0 \\ (n+2)(n+3)=0 \\ n+2=0 \ \ \ \ or \ \ \ \ n+3=0\\n=-2 \ \ \ \ \ \ \ \ \ \ \ \  \   n=-3

Answer: n=-2, n=-3
zaharov [31]3 years ago
3 0
-4n^2-2n=-6-7n-5n^2

Add 5n^2 to both sides
n^2-2n=-6-7n

Add 7n to both sides
n^2+5n=-6

Add 6 to both sides to put the equation into quadratic standard form
n^2+5n+6=0

Factor the equation
(n+2)(n+3)=0

3+2=5, 3*2=6

To solve a quadratic equation, find roots using Zero Product Property.
(n+2)=0
n=-2

(n+3)=0
n=-3

Final answers: n=-3, n=-2
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Step-by-step explanation:

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Answer:

a) 0.0951

b) 0.8098

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Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

In this problem, we have that:

\mu = 26, \sigma = 6, n = 62, s = \frac{6}{\sqrt{62}} = 0.762

(a)

What is the likelihood the sample mean is at least $27.00?

This is 1 subtracted by the pvalue of Z when X = 27. So

Z = \frac{X - \mu}{\sigma}

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{27 - 26}{0.762}

Z = 1.31

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1 - 0.9049 = 0.0951

(b)

What is the likelihood the sample mean is greater than $25.00 but less than $27.00?

This is the pvalue of Z when X = 27 subtracted by the pvalue of Z when X = 25. So

X = 27

Z = \frac{X - \mu}{s}

Z = \frac{27 - 26}{0.762}

Z = 1.31

Z = 1.31 has a pvalue of 0.9049

X = 25

Z = \frac{X - \mu}{s}

Z = \frac{25 - 26}{0.762}

Z = -1.31

Z = -1.31 has a pvalue of 0.0951

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50 - 90/2 = 5

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Between the 5th and the 95th percentile.

5th percentile

X when Z has a pvalue of 0.05. So X when Z = -1.645

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95th percentile

X when Z has a pvalue of 0.95. So X when Z = 1.645

Z = \frac{X - \mu}{s}

1.645 = \frac{X - 26}{0.762}

X - 26 = 1.645*0.762

X = 27.25

Between $24.75 and $27.25.

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