Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

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 \(6000 / 100\).)

Short Answer

Expert verified
The approximate probability that the total weight of the passengers' baggage will exceed the limit is virtually 0.

Step by step solution

01

Calculate the average weight limit per passenger

This problem refers to the average weight limit per passenger, which is obtained by dividing the total baggage limit by the total number of passengers. Let's denote this as \(\bar{x}_{limit}\). Hence, \(\bar{x}_{limit} = \frac{6000}{100} = 60\) lbs.
02

Calculate the mean and the standard deviation of the total weight

According to the Central Limit Theorem, the sum or average of a large number of independent and identically distributed random variables will have a distribution that is approximately normal. Here, each passenger's baggage weight can be seen as one such random variable. Therefore, the mean and the standard deviation of the passengers' total weight, when normalized to the number of passengers, would also be the mean and the standard deviation of this variable. That's \(\mu = 50 lbs\) and \(\sigma = \frac{20 lbs}{\sqrt{100}} = 2 lbs\) respectively.
03

Convert to a Z-Score

The Z-score is a way of standardizing a random variable. It counts the number of standard deviations a data point (in this case, \(\bar{x}_{limit}\)) is away from the mean. For our case, the Z-score will be \( Z = \frac{{\bar{x}_{limit} - \mu}}{{\sigma}} = \frac{{60 lbs - 50 lbs}}{{2 lbs}} = 5 \)
04

Calculate the probability

The probability that the total weight of the baggage will exceed the limit is the same as the probability that Z is more than 5. However, for standard normal tables, it reaches a maximum around Z = 3.4. Beyond this, the area under the curve virtually becomes 1. Hence, we can say that the probability is virtually 0 that the weight will exceed the limit.

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 Vaia!

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

The article "Should Pregnant Women Move? Linking Risks for Birth Defects with Proximity to Toxic Waste Sites" (Chance [1992]: 40-45) reported that in a large study carried out in the state of New York, approximately \(30 \%\) of the study subjects lived within 1 mi of a hazardous waste site. Let \(\pi\) denote the proportion of all New York residents who live within 1 mi of such a site, and suppose that \(\pi=.3\). a. Would \(p\) based on a random sample of only 10 residents have approximately a normal distribution? Explain why or why not. b. What are the mean value and standard deviation of \(p\) based on a random sample of size \(400 ?\) c. When \(n=400\), what is \(P(.25 \leq p \leq .35)\) ? d. Is the probability calculated in Part (c) larger or smaller than would be the case if \(n=500 ?\) Answer without actually calculating this probability.

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.)

A random sample is to be selected from a population that has a proportion of successes \(\pi=.65 .\) Determine the mean and standard deviation of the sampling distribution of \(p\) for each of the following sample sizes: a. \(n=10\) b. \(n=20\) c. \(n=30\) d. \(n=50\) e. \(n=100\) f. \(n=200\)

The article "Thrillers" (Newsweek, April 22,1985 ) stated, "Surveys tell us that more than half of America's college graduates are avid readers of mystery novels." Let \(\pi\) denote the actual proportion of college graduates who are avid readers of mystery novels. Consider a sample proportion \(p\) that is based on a random sample of 225 college graduates. a. If \(\pi=.5\), what are the mean value and standard deviation of \(p ?\) Answer this question for \(\pi=.6\). Does \(p\) have approximately a normal distribution in both cases? Explain. b. Calculate \(P(p \geq .6)\) for both \(\pi=.5\) and \(\pi=.6\). c. Without doing any calculations, how do you think the probabilities in Part (b) would change if \(n\) were 400 rather than \(225 ?\)

Consider a population consisting of the following five values, which represent the number of video rentals during the academic year for each of five housemates: \(\begin{array}{lll} 8 & 14&16 & 10 & 11\end{array}\) a. Compute the mean of this population. b. Select a random sample of size 2 by writing the numbers on slips of paper, mixing them, and then selecting \(2 .\) Compute the mean of your sample. c. Repeatedly select samples of size 2 , and compute the \(\bar{x}\) value for each sample until you have the results of 25 samples. d. Construct a density histogram using the \(25 \bar{x}\) values. Are most of the \(\bar{x}\) values near the population mean? Do the \(\bar{x}\) values differ a lot from sample to sample, or do they tend to be similar?

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.

Sign-up for free