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love history [14]
2 years ago
7

Assume that the weight loss for the first month of a diet program varies between 6 pounds and 14 pounds, and is spread evenly ov

er the range of possibilities, so that there is a uniform distribution. Find the probability of the given range of pounds lost.
1) P(x = 7)

2) Between 8.25 po9unds and 12 pounds.

3) Greater than 10.50 pounds.

Please show work.
Mathematics
1 answer:
Llana [10]2 years ago
5 0

Hello!

For this uniform distribution, the shape of the density function is a line. We can begin by identifying key elements of the distribution:

Range: 6 to 14 pounds

Formula of density function:

y = 1/Range

1 - (14 - 6) = 1/8 = 0.125

f(x) = 0.125

1)

P(x = 7)

A uniform distribution is a continuous distribution, so the probability of x being an EXACT value is approximately 0.

<u>This is the same as if we took the following integral. (Finding the area under a curve with the same start and end point)</u>
\int\limits^a_a {f(x)} \, dx  = 0

Therefore, P(x = 7) = 0.

2)
We can think of this as finding the area of a rectangle.

The width would be the difference between 8.25 pounds and 12 pounds:
W = 12 - 8.25 = 3.75

The height would be the function (y = .125), or:
h = .125

Using the equation for the area of a rectangle: (A = h * w)

A = 3.75 * .125 = 0.469

Thus, P(8.25 < x < 12) = 0.469

3)
To find the probability GREATER than 10.50 pounds, we can subtract this value from the max value for the width:
W = 14 - 10.50 = 3.50

The height is the same as above:
h = .125

Solve for the area:
A = 3.50 * .125 = 0.4375

P(x > 10.5) = 0.4375

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The amount of coffee that a filling machine puts into an 8-ounce jar is normally distributed with a mean of 8.2 ounces and a sta
nordsb [41]

Answer:

73.30% probability that the sampling error made in estimating the mean amount of coffee for all 8-ounce jars by the mean of a random sample of 100 jars will be at most 0.02 ounce

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 = 8.2, \sigma = 0.18, n = 100, s = \frac{0.18}{\sqrt{100}} = 0.018

What is the probability that the sampling error made in estimating the mean amount of coffee for all 8-ounce jars by the mean of a random sample of 100 jars will be at most 0.02 ounce?

This is the pvalue of Z when X = 8.2 + 0.02 = 8.22 subtracted by the pvalue of Z when X = 8.2 - 0.02 = 8.18. So

X = 8.22

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

By the Central Limit Theorem

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

Z = \frac{8.22 - 8.2}{0.018}

Z = 1.11

Z = 1.11 has a pvalue of 0.8665

X = 8.18

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

Z = \frac{8.18 - 8.2}{0.018}

Z = -1.11

Z = -1.11 has a pvalue of 0.1335

0.8665 - 0.1335 = 0.7330

73.30% probability that the sampling error made in estimating the mean amount of coffee for all 8-ounce jars by the mean of a random sample of 100 jars will be at most 0.02 ounce

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