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ExtremeBDS [4]
4 years ago
13

QUESTION 9 Imagine that the mean height for all Division I women's basketball programs is 69 inches with a standard deviation of

3 inches. The 2010–2011 women's basketball team at the University of Connecticut, with 10 players listed on the roster, had an average height of 71.2 inches. Using the z statistic, what percent of means would fall below that for these UConn Huskies? (Round z score to two decimal places.)
Mathematics
1 answer:
fenix001 [56]4 years ago
3 0

Answer:

98.98% of means would fall below that for these UConn Huskies

Step-by-step explanation:

To solve this qustion, 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 random variable X, with mean \mu and standard deviation \sigma, the sample means with size n of at least 30 can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}

In this problem, we have that:

\mu = 69, \sigma = 3, n = 10, s = \frac{3}{\sqrt{10}} = 0.9487

The 2010–2011 women's basketball team at the University of Connecticut, with 10 players listed on the roster, had an average height of 71.2 inches. Using the z statistic, what percent of means would fall below that for these UConn Huskies?

This is the pvalue of Z when X = 71.2. So

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

By the Central limit theorem

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

Z = \frac{71.2 - 69}{0.9487}

Z = 2.32

Z = 2.32 has a pvalue of 0.9898

98.98% of means would fall below that for these UConn Huskies

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Answer:

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Step-by-step explanation:

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3 years ago
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Find the length of the third side. If necessary, round to the nearest tenth. 5 10 ​
snow_lady [41]

Answer:

\boxed {\boxed {\sf 8.7}}

Step-by-step explanation:

We are asked to find the length of the third side in a triangle, given the other 2 sides.

Since this is a right triangle (note the small square in the corner of the triangle representing a 90 degree /right angle), we can use the Pythagorean Theorem.

a^2 + b^2 =c^2

In this theorem, <em>a</em> and <em>b</em> are the legs of the triangle and <em>c</em> is the hypotenuse.

We know that the unknown side (we can say it is a) and the side measuring 5 are the legs because they form the right angle. The side measuring 10 is the hypotenuse because it is opposite the right angle.

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Substitute the values into the formula.

a^2 + (5)^2 = (10)^2

Solve the exponents.

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a^2 + 25=100

We are solving for a, so we must isolate the variable. 25 is being added to a. The inverse operation of addition is subtraction, so we subtract 25 from both sides.

a^2 +25-25=100-25

a^2=100-25

a^2 = 75

a is being squared. The inverse of a square is the square root, so we take the square root of both sides.

\sqrt {a^2}= \sqrt{75}

a= \sqrt{75}

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Round to the nearest tenth. The 6 in the hundredth place tells us to round the 6 up to a 7 in the tenth place.

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cupoosta [38]

Answer:

The upper endpoint of the 99% confidence interval for population proportion is 0.13.

Step-by-step explanation:

The confidence interval for population proportion is:

CI=\hat p\pm z_{\alpha /2}\sqrt{\frac{\hat p(1-\hat p)}{n}}

<u>Given:</u>

<em>n</em> = 1000

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*Use the standard normal table for the critical value.

Compute the 99% confidence interval for population proportion as follows:

CI=\hat p\pm z_{\alpha /2}\sqrt{\frac{\hat p(1-\hat p)}{n}}\\=0.102\pm 2.58\times\sqrt{\frac{0.102(1-0.102)}{1000}}\\=0.102\pm0.0248\\=(0.0772, 0.1268)\\\approx (0.08, 0.13)

Thus, the upper limit of the 99% confidence interval for population proportion is 0.13.

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Your answer
yan [13]

Answer:

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