/*! 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 19 Use the data in the table below ... [FREE SOLUTION] | 91Ó°ÊÓ

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Use the data in the table below for sitting adult males and females (based on anthropometric survey data from Gordon, Churchill, et al.). These data are used often in the design of different seats, including aircraft seats, train seats, theater seats, and classroom seats. (Hint: Draw a graph in each case.) $$\begin{array}{|l|l|l|l|} \hline & \text { Mean } & \text { St. Dev. } & \text { Distribution } \\ \hline \text { Males } & 23.5 \mathrm{in} . & 1.1 \mathrm{in} . & \text { Normal } \\ \hline \text { Females } & 22.7 \mathrm{in} . & 1.0 \mathrm{in} . & \text { Normal } \\ \hline \end{array}$$ Significance Instead of using 0.05 for identifying significant values, use the criteria that a value \(x\) is significantly high if \(P(x \text { or greater) } \leq 0.01\) and a value is significantly low if \(P(x \text { or less }) \leq 0.01 .\) Find the back-to-knee lengths for males, separating significant values from those that are not significant. Using these criteria, is a male back-to- knee length of 26 in. significantly high?

Short Answer

Expert verified
No, 26 in. is not significantly high for a male back-to-knee length since the probability 0.0116 is greater than 0.01.

Step by step solution

01

Understand the distribution and given data

The problem provides data for sitting adult males and describes the distribution as normal with a mean of 23.5 in. and a standard deviation of 1.1 in. We need to determine if a male back-to-knee length of 26 in. is significantly high using the criteria that a value is considered significantly high if the probability of observing such a value or greater is less than or equal to 0.01.
02

Standardize the value

Use the Z-score formula to standardize the value of 26 in. The Z-score formula is \[ Z = \frac{x - \mu}{\sigma} \] where \( x \) is the value, \( \mu \) is the mean, and \( \sigma \) is the standard deviation. Substituting the given values,\[ Z = \frac{26 - 23.5}{1.1} = \frac{2.5}{1.1} \approx 2.27 \]
03

Find the probability associated with the Z-score

Use the Z-score obtained in the previous step to find the probability from the standard normal distribution table. The Z-score of 2.27 corresponds to a cumulative probability of approximately 0.9884. This means that the probability of a back-to-knee length being less than 26 in. is 0.9884.
04

Calculate the probability for significant high

To find the probability of a back-to-knee length being 26 in. or greater, subtract the cumulative probability from 1. \[ P(X \geq 26) = 1 - P(X < 26) = 1 - 0.9884 = 0.0116 \] Compare this with the significance level of 0.01.
05

Determine significance

Since 0.0116 is greater than 0.01, the probability of having a back-to-knee length of 26 in. or greater is not less than or equal to 0.01. Therefore, 26 in. is not considered significantly high.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Normal Distribution
The concept of a normal distribution is crucial in statistics. It is a type of continuous probability distribution that is symmetrical around its mean, indicating that data near the mean are more frequent in occurrence than data far from the mean. The shape of a normal distribution is often referred to as a 'bell curve'. For instance, in our exercise, both males and females' back-to-knee lengths follow a normal distribution. The mean (average) values are different for each group (23.5 inches for males and 22.7 inches for females), but they both have a similar bell-shaped curve. Understanding normal distribution helps us analyze probabilities and make inferences about data patterns.
Z-score Calculation
A Z-score is a statistical measurement that describes a value's relation to the mean of a group of values. It indicates how many standard deviations an element is from the mean. The formula for calculating the Z-score is: \[ Z = \frac{x - \mu}{\sigma} \] where \( x \) is the value, \( \mu \) is the mean, and \( \sigma \) is the standard deviation. For example, in our exercise, to determine if a back-to-knee length of 26 inches for males is significantly high, we calculate the Z-score. Substituting the given values: \[ Z = \frac{26 - 23.5}{1.1} = \frac{2.5}{1.1} \approx 2.27 \] This Z-score tells us that 26 inches is about 2.27 standard deviations away from the mean.
Significance Testing
Significance testing is a statistical technique used to identify whether a result is statistically significant. In our exercise, we use a significance level of 0.01 to determine if a back-to-knee length is significantly high or low. This means a value is significantly high if the probability of observing it or greater is less than or equal to 0.01. After calculating the Z-score, we check the cumulative probability from the standard normal distribution table. For example, a Z-score of 2.27 corresponds to a cumulative probability of approximately 0.9884. Thus, the probability of a back-to-knee length being 26 inches or greater is calculated as: \[ P(X \geq 26) = 1 - P(X < 26) = 1 - 0.9884 = 0.0116 \] Since 0.0116 is greater than 0.01, 26 inches is not considered significantly high.
Probability
Probability is the measure of the likelihood that an event will occur. In our exercise, determining the probability helps conclude whether a certain male back-to-knee length is significantly high. After finding the Z-score, we use the standard normal distribution table to find the cumulative probability associated with the Z-score. For example, a Z-score of 2.27 gives us a cumulative probability of 0.9884. This means there’s a 98.84% chance of a back-to-knee length being less than 26 inches. To find the probability of the length being 26 inches or more, we subtract this value from 1: \[ P(X \geq 26) = 1 - 0.9884 = 0.0116 \] This probability helps us in significance testing and making decisions based on statistical evidence.

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Most popular questions from this chapter

When a water taxi sank in Baltimore's Inner Harbor, an investigation revealed that the safe passenger load for the water taxi was 3500 ib It was also noted that the mean weight of a passenger was assumed to be 140 ib. Assume a "worst- case" scenario in which all of the passengers are adult men. Assume that weights of men are normally distributed with a mean of 188.6 Ib and a standard deviation of 38.9 Ib (based on Data Set 1 "Body Data" in Appendix B). a. If one man is randomly selected, find the probability that he weighs less than 174 lb (the new value suggested by the National Transportation and Safety Board). b. With a load limit of 3500 ib, how many male passengers are allowed if we assume a mean weight of 140 lb? c. With a load limit of 3500 ib, how many male passengers are allowed if we assume the updated mean weight of 188.6 lb? d. Why is it necessary to periodically review and revise the number of passengers that are allowed to board?

Annual Incomes Annual incomes are known to have a distribution that is skewed to the right instead of being normally distributed. Assume that we collect a large \((n>30)\) random sample of annual incomes. Can the distribution of incomes in that sample be approximated by a normal distribution because the sample is large? Why or why not?

Sampling Distribution Data Set 4 "Births" in Appendix B includes a sample of birth weights. If we explore this sample of 400 birth weights by constructing a histogram and finding the mean and standard deviation, do those results describe the sampling distribution of the mean? Why or why not?

Sampling with Replacement The Orangetown Medical Research Center randomly selects 100 births in the United States each day, and the proportion of boys is recorded for each sample. a. Do you think the births are randomly selected with replacement or without replacement? b. Give two reasons why statistical methods tend to be based on the assumption that sampling is conducted with replacement, instead of without replacement.

Do the following: If the requirements of \(n p \geq 5\) and \(n q \geq 5\) are both satisfied, estimate the indicated probability by using the normal distribution as an approximation to the binomial distribution; if \(n p < 5\) or n \(q < 5,\) then state that the normal approximation should not be used. With \(n=8\) births and \(p=0.512\) for a boy, find \(P\) (exactly 5 boys).

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