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Naddik [55]
3 years ago
14

Evaluate the integral by making the given substitution. (use c for the constant of integration.) sec2(1/x9) x10 dx, u = 1/x9

Mathematics
1 answer:
Firlakuza [10]3 years ago
5 0

Answer:

\frac{1}{-9}tan \frac{1}{x^9} + c

Step-by-step explanation:

The question requires addition information. Below is how the question should be rightly stated.

        \int\limits {\frac{sec^{2}(\frac{1}{x^9})}{ x^{10} } } \, dx   ------(1)

Using substitution method,

We are given u =\frac{1}{x^{9}}                   -------(2)

                      ⇒ u =x^{-9}

Differentiating u with respect to x,

                      \frac{du}{dx} = -9x^{-9-1}

                      \frac{du}{dx} = -9x^{-10}

Making dx the subject of the equation

                      dx =\frac{du}{-9x^{-10}}

                      dx =\frac{x^{10}du}{-9}                         ---------(3)

Substituting the values of u from equation (2) and dx from equation (3) into equation (1)

                      \int\limits {\frac{sec^{2}u}{x^{10}}} . \frac{x^{10}}{-9} \, du

                      \int\limits {\frac{sec^{2}u}{-9}} \, du

                      \frac{1}{-9}\int\limits {sec^{2}u \, du

                      \frac{1}{-9}tanu + c

substituting the value of u

                      \frac{1}{-9}tan \frac{1}{x^9} + c

 

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Answer:

a) 0.0951

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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

Z = 1.31 has a pvalue of 0.9049

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

0.9049 - 0.0951 = 0.8098

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

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

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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

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