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Kobotan [32]
3 years ago
5

The length of a rectangle is eight more

Mathematics
1 answer:
tatyana61 [14]3 years ago
3 0

Answer:

Width of the rectangle =  13.16 ft

Length of the rectangle =  34.33 ft

Step-by-step explanation:

Let the width of the rectangle = k ft

So, the length of the rectangle =  (2k + 8) ft

Perimeter of the rectangle = 95 ft

PERIMETER OF THE RECTANGLE = 2(LENGTH +WIDTH)

⇒ 2( k+ (2k+ 8))  = 95

or,  2( 3k + 8) = 95  ⇒  6k + 16 = 95

or, 6k = 95 - 16 = 79

⇒ k = 79/ 6 = 13.16

So, the Width of the rectangle = k = 13.16 ft

Now, Length of the rectangle = 2(13.16)+ 8 = 34.33 ft

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Shelia's measured glucose level one hour after a sugary drink varies according to the normal distribution with μ = 117 mg/dl and
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Answer:

The level L such that there is probability only 0.01 that the mean glucose level of 6 test results falls above L is L = 127.1 mg/dl.

Step-by-step explanation:

To solve this problem, 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, a large sample size can be approximated to a normal distribution with mean \mu and standard deviation \frac{\sigma}{\sqrt{n}}

In this problem, we have that:

\mu = 117, \sigma = 10.6, n = 6, s = \frac{10.6}{\sqrt{6}} = 4.33

What is the level L such that there is probability only 0.01 that the mean glucose level of 6 test results falls above L ?

This is the value of X when Z has a pvalue of 1-0.01 = 0.99. So X when Z = 2.33.

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

By the Central Limit Theorem

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

2.33 = \frac{X - 117}{4.33}

X - 117 = 2.33*4.33

X = 127.1

The level L such that there is probability only 0.01 that the mean glucose level of 6 test results falls above L is L = 127.1 mg/dl.

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