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wolverine [178]
3 years ago
14

Factorise fully 4x^2+28x^3

Mathematics
2 answers:
olga_2 [115]3 years ago
8 0

Answer:

4x²×(1+7x)

Step-by-step explanation:

Novosadov [1.4K]3 years ago
4 0

Answer:

4x^2+28x^3 : 4x^2 (7x + 1)

Step-by-step explanation:

4x^2+28x^3

Apply exponent rule: \:a^{b+c}=a^ba^c

x^3=xx^2

=28xx^2+4x^2

Rewrite 28 as 4\cdot \:7:

=4\cdot \:7xx^2+4x^2

Factor out common term 4x^2:

=4x^2\left(7x+1\right)

Hope I helped, if so may I get brainliest and a thanks?

Thank you, have a good day! =)

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Answer:

a) 0.0016 = 0.16% probability that the sample mean is at least $30.00.

b) 0.8794 = 87.94% probability that the sample mean is greater than $26.50 but less than $30.00

c) 90% of sample means will occur between $26.1 and $28.9.

Step-by-step explanation:

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

Normal probability distribution

When the distribution is normal, we use 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 question, we have that:

\mu = 27.50, \sigma = 7, n = 68, s = \frac{7}{\sqrt{68}} = 0.85

a. What is the likelihood the sample mean is at least $30.00?

This is 1 subtracted by the pvalue of Z when X = 30. So

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

By the Central Limit Theorem, we have that:

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

Z = \frac{30 - 27.5}{0.85}

Z = 2.94

Z = 2.94 has a pvalue of 0.9984

1 - 0.9984 = 0.0016

0.0016 = 0.16% probability that the sample mean is at least $30.00.

b. What is the likelihood the sample mean is greater than $26.50 but less than $30.00?

This is the pvalue of Z when X = 30 subtracted by the pvalue of Z when X = 26.50. So

From a, when X = 30, Z has a pvalue of 0.9984

When X = 26.5

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

Z = \frac{26.5 - 27.5}{0.85}

Z = -1.18

Z = -1.18 has a pvalue of 0.1190

0.9984 - 0.1190 = 0.8794

0.8794 = 87.94% probability that the sample mean is greater than $26.50 but less than $30.00.

c. Within what limits will 90 percent of the sample means occur?

Between the 50 - (90/2) = 5th percentile and the 50 + (90/2) = 95th percentile, that is, Z between -1.645 and Z = 1.645

Lower bound:

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

-1.645 = \frac{X - 27.5}{0.85}

X - 27.5 = -1.645*0.85

X = 26.1

Upper Bound:

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

1.645 = \frac{X - 27.5}{0.85}

X - 27.5 = 1.645*0.85

X = 28.9

90% of sample means will occur between $26.1 and $28.9.

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