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melisa1 [442]
3 years ago
10

WILL MARK BRAINLIEST consider the polynomial expression below x^3-3x^2+81x-243 rewrite the polynomial in the form (x-d)(x-e)(x+f

) where d is a real number, and e and f are complex numbers in the form bi

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

Answer:

x³ - 3x² + 81x - 243  = ( x - 3 )( x - 9i )( x + 9i )

Step-by-step explanation:

Given Expression is x³ - 3x² + 81x - 243

We need to factorize the given expression in form of ( x - d )( x - e )( x + f ) where d is a real number, and e and f are complex numbers in the form bi.

From Rational factor theorem,

Factors of 243 is ±1 , ±3 , ±9 , ±27 , ±81 , ±243.

Now by hit and trial we find first factor of real number.

let, p(x) = x³ - 3x² + 81x - 243

put x = 3

p(3) = 3³ - 3 × 3² + 81 × 3 - 243  = 27 - 27 + 243 - 243 = 0

So, The first factor is ( x - 3 ).

Now to find other factor we divide given polynomial with x - 3.

Division is attached.

We get Quotient = x² + 81

Remainder = 0

Now, Factorize x² + 81 = ( x )² - ( 9i )² = ( x - 9i )( x + 9i )

Therefore, x³ - 3x² + 81x - 243  = ( x - 3 )( x - 9i )( x + 9i )

Natali [406]3 years ago
3 0

Answer:

(x - 3)(x - 9i)(x + 9i)

Step-by-step explanation:

note that x = 3 gives

3³ - 3(3)² + 81(3) - 243 = 27 - 27 + 243 - 243 = 0, hence

x = 3 is a root and (x - 3) is a factor

x³ - 3x² + 81x - 243 ÷ (x - 3)

= (x - 3)(x² + 81)

solve x² + 81 = 0 ⇒ x² = - 81 ⇒ x = ± 9i, thus

x³ - 3x² + 81x - 243 = (x - 3)(x - 9i)(x + 9i) ← in factored form




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a) 0.6212 = 62.12% probability that the mean price for a sample of 30 federal income tax returns is within $16 of the population mean.

b) 0.7416 = 74.16% probability that the mean price for a sample of 50 federal income tax returns is within $16 of the population mean.

c) 0.8804 = 88.04% probability that the mean price for a sample of 100 federal income tax returns is within $16 of the population mean.

d) None of them ensure, that one which comes closer is a sample size of 100 in option c), to guarantee, we need to keep increasing the sample size.

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 normal distributions can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes 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.

The CPA Practice Advisor reports that the mean preparation fee for 2017 federal income tax returns was $273. Use this price as the population mean and assume the population standard deviation of preparation fees is $100.

This means that \mu = 273, \sigma = 100

A) What is the probability that the mean price for a sample of 30 federal income tax returns is within $16 of the population mean?

Sample of 30 means that n = 30, s = \frac{100}{\sqrt{30}}

The probability is the p-value of Z when X = 273 + 16 = 289 subtracted by the p-value of Z when X = 273 - 16 = 257. So

X = 289

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

By the Central Limit Theorem

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

Z = \frac{289 - 273}{\frac{100}{\sqrt{30}}}

Z = 0.88

Z = 0.88 has a p-value of 0.8106

X = 257

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

Z = \frac{257 - 273}{\frac{100}{\sqrt{30}}}

Z = -0.88

Z = -0.88 has a p-value of 0.1894

0.8106 - 0.1894 = 0.6212

0.6212 = 62.12% probability that the mean price for a sample of 30 federal income tax returns is within $16 of the population mean.

B) What is the probability that the mean price for a sample of 50 federal income tax returns is within $16 of the population mean?

Sample of 30 means that n = 50, s = \frac{100}{\sqrt{50}}

X = 289

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

By the Central Limit Theorem

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

Z = \frac{289 - 273}{\frac{100}{\sqrt{50}}}

Z = 1.13

Z = 1.13 has a p-value of 0.8708

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

Z = \frac{257 - 273}{\frac{100}{\sqrt{50}}}

Z = -1.13

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C) What is the probability that the mean price for a sample of 100 federal income tax returns is within $16 of the population mean?

Sample of 30 means that n = 100, s = \frac{100}{\sqrt{100}}

X = 289

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

By the Central Limit Theorem

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

Z = \frac{289 - 273}{\frac{100}{\sqrt{100}}}

Z = 1.6

Z = 1.6 has a p-value of 0.9452

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Z = \frac{257 - 273}{\frac{100}{\sqrt{100}}}

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D) Which, if any of the sample sizes in part (a), (b), and (c) would you recommend to ensure at least a .95 probability that the same mean is withing $16 of the population mean?

None of them ensure, that one which comes closer is a sample size of 100 in option c), to guarantee, we need to keep increasing the sample size.

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