/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 35 Water permeability of concrete c... [FREE SOLUTION] | 91Ó°ÊÓ

91Ó°ÊÓ

Water permeability of concrete can be measured by letting water flow across the surface and determining the amount lost (in inches per hour). Suppose that the permeability index \(x\) for a randomly selected concrete specimen of a particular type is normally distributed with mean value 1000 and standard deviation 150 . a. How likely is it that a single randomly selected specimen will have a permeability index between 850 and \(1300 ?\) b. If the permeability index is to be determined for each specimen in a random sample of size 10 , how likely is it that the sample average permeability index will be between 950 and \(1100 ?\) between 850 and 1300 ?

Short Answer

Expert verified
a. The probability that a single randomly selected specimen will have a permeability index between 850 and 1300 is the calculated probability from step 1. \n b. The probability that the sample average permeability index will be between 950 and 1100, and between 850 and 1300 in a sample size of 10 are the calculated probabilities from step 2.

Step by step solution

01

Find Probability for Part a

First, calculate the z-scores for the values 850 and 1300. The formula for z-score is \( z = (X - \mu) / \sigma \). Using this formula, calculate the z-score for 850 (\( z_1 \)) and for 1300 (\( z_2 \)). Next, refer to the standard normal distribution table to find the probabilities corresponding to the \( z_1 \) and \( z_2 \). The area between these two z-scores in the standard normal distribution curve represents the desired probability.
02

Find Probability for Part b

Repeat the processes in step 1 by considering a sample size of 10. However, keep in mind that when you are dealing with the average of a sample, the standard deviation is divided by the square root of the sample size (this is the standard error). Now, calculate the z-scores for the values 950 and 1100, in the same way as in step 1, using the formula for z-score and standard error. Moreover, make identical calculation for the range 850 to 1300. Refer to the standard normal distribution table to determine the probabilities.
03

Interpret the Results

The probabilities obtained from the z-scores indicate the likelihood of the permeability index falling within the given ranges. Therefore, the results from step 1 represent answer for part a of the question, and the results from step 2 represent answer for part b.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

A certain chromosome defect occurs in only 1 out of 200 adult Caucasian males. A random sample of \(n=100\) adult Caucasian males is to be obtained. a. What is the mean value of the sample proportion \(p\), and what is the standard deviation of the sample proportion? b. Does \(p\) have approximately a normal distribution in this case? Explain. c. What is the smallest value of \(n\) for which the sampling distribution of \(p\) is approximately normal?

Suppose that a sample of size 100 is to be drawn from a population with standard deviation \(10 .\) a. What is the probability that the sample mean will be within 2 of the value of \(\mu\) ? b. For this example \((n=100, \sigma=10)\), complete each of the following statements by computing the appropriate value: i. Approximately 95% of the time, \(\bar{x}\) will be within _____ of \(\mu .\) ii. Approximately 0.3% of the time, \(\bar{x}\) will be farther than _____ from\(\mu .\)

The thickness (in millimeters) of the coating applied to disk drives is a characteristic that determines the usefulness of the product. When no unusual circumstances are present, the thickness \((x)\) has a normal distribution with a mean of \(3 \mathrm{~mm}\) and a standard deviation of \(0.05\) \(\mathrm{mm}\). Suppose that the process will be monitored by selecting a random sample of 16 drives from each shift's production and determining \(\bar{x}\), the mean coating thickness for the sample. a. Describe the sampling distribution of \(\bar{x}\) (for a sample of size 16 ). b. When no unusual circumstances are present, we expect \(\bar{x}\) to be within \(3 \sigma_{\bar{x}}\) of \(3 \mathrm{~mm}\), the desired value. An \(\bar{x}\) value farther from 3 than \(3 \sigma_{\bar{x}}\) is interpreted as an indication of a problem that needs attention. Compute \(3 \pm 3 \sigma_{\bar{x}}\). (A plot over time of \(\bar{x}\) values with horizontal lines drawn at the limits \(\mu \pm 3 \sigma_{\bar{x}}\) is called a process control chart.) c. Referring to Part (b), what is the probability that a sample mean will be outside \(3 \pm 3 \sigma_{\bar{x}}\) just by chance (i.e., when there are no unusual circumstances)? d. Suppose that a machine used to apply the coating is out of adjustment, resulting in a mean coating thickness of \(3.05 \mathrm{~mm}\). What is the probability that a problem will be detected when the next sample is taken? (Hint: This will occur if \(\bar{x}>3+3 \sigma_{\bar{x}}\) or \(\bar{x}<3-3 \sigma_{\bar{x}}\) when \(\mu=\) 3.05.) b. When no unusual circumstances are present, we expect \(\bar{x}\) to be within \(3 \sigma_{\bar{x}}\) of \(3 \mathrm{~mm}\), the desired value. An \(\bar{x}\) value farther from 3 than \(3 \sigma_{\bar{x}}\) is interpreted as an indication of a problem that needs attention. Compute \(3 \pm 3 \sigma_{\bar{x}}\). (A plot over time of \(\bar{x}\) values with horizontal lines drawn at the limits \(\mu \pm 3 \sigma_{\bar{x}}\) is called a process control chart.)

Consider the following population: \(\\{2,3,3,4,4\\}\). The value of \(\mu\) is \(3.2\), but suppose that this is not known to an investigator, who therefore wants to estimate \(\mu\) from sample data. Three possible statistics for estimating \(\mu\) are Statistic \(1:\) the sample mean, \(\bar{x}\) Statistic 2 : the sample median Statistic 3 : the average of the largest and the smallest values in the sample A random sample of size 3 will be selected without replacement. Provided that we disregard the order in which the observations are selected, there are 10 possible samples that might result (writing 3 and \(3^{*}, 4\) and \(4^{*}\) to distinguish the two 3 's and the two 4 's in the population): $$\begin{array}{rlllll} 2,3,3^{*} & 2,3,4 & 2,3,4^{*} & 2,3^{*}, 4 & 2,3^{*}, 4^{*} \\ 2,4,4^{*} & 3,3^{*}, 4 & 3,3^{*}, 4^{*} & 3,4,4^{*} & 3^{*}, 4,4^{*} \end{array}$$ For each of these 10 samples, compute Statistics 1,2, and 3\. Construct the sampling distribution of each of these statistics. Which statistic would you recommend for estimating \(\mu\) and why?

An airplane with room for 100 passengers has a total baggage limit of 6000 lb. Suppose that the total weight of the baggage checked by an individual passenger is a random variable \(x\) with a mean value of \(50 \mathrm{lb}\) and a standard deviation of \(20 \mathrm{lb}\). If 100 passengers will board a flight, what is the approximate probability that the total weight of their baggage will exceed the limit? (Hint: With \(n=100\), the total weight exceeds the limit when the average weight \(\bar{x}\) exceeds \(6000 / 100\).)

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.