/*! 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} Q34E Exposure to microbial products, ... [FREE SOLUTION] | 91影视

91影视

Exposure to microbial products, especially endotoxin, may have an impact on vulnerability to allergic diseases. The article 鈥淒ust Sampling Methods for Endotoxin鈥擜n Essential, But Underestimated Issue鈥 (Indoor Air,2006: 20鈥27) considered various issues associated with determining endotoxin concentration. The following data on concentration (EU/mg) in settled dust for one sample of urban homes and another of farm homes was kindly supplied by the authors of the cited article.

U: 6.0 5.0 11.0 33.0 4.0 5.0 80.0 18.0 35.0 17.0 23.0

F: 4.0 14.0 11.0 9.0 9.0 8.0 4.0 20.0 5.0 8.9 21.0

9.2 3.0 2.0 0.3

  1. Determine the sample mean for each sample. How do they compare?
  2. Determine the sample median for each sample. How do they compare? Why is the median for the urban sample so different from the mean for that sample?
  3. Calculate the trimmed mean for each sample by deleting the smallest and largest observation. What are the corresponding trimming percentages? How do the values of these trimmed means compare to the corresponding means and medians?

Short Answer

Expert verified

a. The sample mean for one sample of urban homes is 21.5.The sample mean for farm homes is 8.6.

b. The sample median for one sample of urban homes is 17.The sample median for farm homes is 8.9.

c. The trimmed sample mean for urban homes is 17 and for another of farm homes is 8.24. The trimmed percentages for urban homes and farm homes are 9.09% and 6.67% respectively.

On comparison, trimmed mean is observed to be lower in magnitude than the corresponding mean and medians for each of the two variables.

Step by step solution

01

Given information

The data on the concentration (EU/mg) in settled dust for one sample of urban homes and another of farm homes are provided.

02

Compute the sample mean

Let x represents the concentration (EU/mg) in settled dust for one sample of urban homes.

Let y represent the concentration (EU/mg) in settled dust for another farm homes.

The sample mean is computed as,

For x,

\(\begin{array}{c}\bar x &=& \frac{{\sum {{x_i}} }}{n}\\ &=& \frac{{6 + 5 + 11 + ... + 23}}{{11}}\\ &=& \frac{{237}}{{11}}\\ &=& 21.5\end{array}\)

Thus, the sample mean for x is 21.5EU/mg.

For y,

\(\begin{array}{c}\bar y &=& \frac{{\sum {{y_i}} }}{{{n_y}}}\\ &=& \frac{{4 + 14 + 11 + ... + 0.3}}{{15}}\\ &=& \frac{{128.4}}{{15}}\\ &=& 8.6\end{array}\)

Thus, the sample mean for y is 8.6EU/mg.

Therefore, it can be concluded that the average concentration (EU/mg) in settled dust for one sample of urban homes is more than compared to the another of farm homes.

03

Compute the sample median

Let x represents the concentration (EU/mg) in settled dust for one sample of urban homes.

Let y represent the concentration (EU/mg) in settled dust for another farm homes.

The sample median is computed by first ordering the data in ascending order.

For x,

The data is arranged as,

4

5

5

6

11

17

18

23

33

35

80

For the odd number of observations, the median value is computed as,

\(\begin{array}{c}\tilde x &=& {\left( {\frac{{n + 1}}{2}} \right)^{th}}ordered\;value\\ &=& {\left( {\frac{{11 + 1}}{2}} \right)^{th}}ordered\;value\\ &=& {\left( 6 \right)^{th}}ordered\;value\end{array}\)

Thus, the median value of x is 17EU/mg.

For y,

The data is arranged as,

0.3

2

3

4

4

5

8

8.9

9

9

9.2

11

14

20

21

For the odd number of observations, the median value is computed as,

\(\begin{array}{c}\tilde y &=& {\left( {\frac{{n + 1}}{2}} \right)^{th}}ordered\;value\\ &=& {\left( {\frac{{15 + 1}}{2}} \right)^{th}}ordered\;value\\ &=& {\left( 8 \right)^{th}}ordered\;value\end{array}\)

Thus, the median value of y is 8.9EU/mg.

Therefore, it can be concluded that the median concentration (EU/mg) in settled dust for one sample of urban homes is more than compared to the another of farm homes.

The median for the urban sample is so different from the mean for that sample because mean represents the average and the median value represents the centre.

04

Compute the trimmed mean

Let x represents the concentration (EU/mg) in settled dust for one sample of urban homes.

Let y represent the concentration (EU/mg) in settled dust for another farm homes.

The trimmed mean for each sample is obtained by deleting few of the smallest and largest observation.

The trimmed data for x is,

5

5

6

11

17

18

23

33

35

The sample mean is computed as,

For x,

\(\begin{array}{c}{{\bar x}_{tr}} &=& \frac{{\sum {{x_i}} }}{n}\\ &=& \frac{{5 + 5 + 6 + ... + 35}}{9}\\ &=& \frac{{153}}{9}\\ &=& 17\end{array}\)

Thus, the trimmed mean for x is 17EU/mg.

The trimmed data for y is,

2

3

4

4

5

8

8.9

9

9

9.2

11

14

20

For y,

\(\begin{array}{c}{{\bar y}_{\left( {tr} \right)}} &=& \frac{{\sum {{y_i}} }}{{{n_y}}}\\ &=& \frac{{2 + 3 + 4 + ... + 20}}{{13}}\\ &=& \frac{{107.1}}{{13}}\\ &=& 8.2\end{array}\)

Thus, the trimmed mean for y is 8.2EU/mg.

05

Compute the trimmed percentages

The trimmed percentages (P) are computed using the following formula for each of the two samples.

\(\frac{P}{{100}}*N = K \Rightarrow {\bf{P = }}\frac{{\bf{K}}}{{\bf{N}}}*{\bf{100}}\)

Where N is the total number of observations and K is the number of values removed from each end.

In case of urban homes,

\(\begin{array}{l}N = 11\\K = 1\end{array}\)

The trimmed percentage is,

\(\begin{array}{c}{P_U} &=& \frac{1}{{11}}*100\\ &=& 9.09\% \end{array}\)

In case of farm homes,

\(\begin{array}{l}N = 15\\K = 1\end{array}\)

The trimmed percentage is,

\(\begin{array}{c}{P_F} &=& \frac{1}{{15}}*100\\ &=& 6.67\% \end{array}\)

On comparison of trimmed mean with the mean of the complete data, it is concluded that the trimmed mean is smaller in magnitude for both urban and farm homes.

Similarly, it is also observed that the trimmed mean is lower in magnitude than the median values.

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 91影视!

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

One piece of PVC pipe is to be inserted inside another piece. The length of the first piece is normally distributed with mean value \({\rm{20}}\) in. and standard deviation \({\rm{.5}}\) in. The length of the second piece is a normal \({\rm{rv}}\)with mean and standard deviation \({\rm{15}}\) in. and \({\rm{.4}}\) in., respectively. The amount of overlap is normally distributed with mean value \({\rm{1}}\) in. and standard deviation \({\rm{.1}}\) in. Assuming that the lengths and amount of overlap are independent of one another, what is the probability that the total length after insertion is between \({\rm{34}}{\rm{.5 }}\) in. and \({\rm{35}}\) in.?

The May 1, 2009, issue of the Mont clarian reported the following home sale amounts for a sample of homes in Alameda, CA that were sold the previous month (1000s of $):

590 815 575 608 350 1285 408 540 555 679

  1. Calculate and interpret the sample mean and median.
  2. Suppose the 6th observation had been 985 rather than 1285. How would the mean and median change?
  3. Calculate a 20% trimmed mean by first trimming the two smallest and two largest observations.
  4. Calculate a 15% trimmed mean.

Grip is applied to produce normal surface forces that compress the object being gripped. Examples include two people shaking hands, or a nurse squeezing a patient鈥檚 forearm to stop bleeding. The article 鈥淚nvestigation of Grip Force, Normal Force, Contact Area, Hand Size, and Handle Size for Cylindrical Handles鈥 (Human Factors, 2008: 734鈥744) included the following data on grip strength (N) for a sample of 42

individuals:

16 18 18 26 33 41 54 56 66 68 87 91 95

98 106 109 111 118 127 127 135 145 147 149 151 168

172 183 189 190 200 210 220 229 230 233 238 244 259

294 329 403

a. Construct a stem-and-leaf display based on repeating each stem value twice, and comment on interesting features.

b. Determine the values of the fourths and the fourth spread.

c. Construct a boxplot based on the five-number summary, and comment on its features.

d. How large or small does an observation have to be to qualify as an outlier? An extreme outlier? Are there any outliers?

e. By how much could the observation 403, currently the largest, be decreased without affecting\({f_s}\)?

Let \({\rm{X}}\) be the total medical expenses (in \({\rm{1000}}\) s of dollars) incurred by a particular individual during a given year. Although \({\rm{X}}\) is a discrete random variable, suppose its distribution is quite well approximated by a continuous distribution with pdf \({\rm{f(x) = k(1 + x/2}}{\rm{.5}}{{\rm{)}}^{{\rm{ - 7}}}}\) for.

a. What is the value of\({\rm{k}}\)?

b. Graph the pdf of \({\rm{X}}\).

c. What are the expected value and standard deviation of total medical expenses?

d. This individual is covered by an insurance plan that entails a \({\rm{\$ 500}}\) deductible provision (so the first \({\rm{\$ 500}}\) worth of expenses are paid by the individual). Then the plan will pay \({\rm{80\% }}\) of any additional expenses exceeding \({\rm{\$ 500}}\), and the maximum payment by the individual (including the deductible amount) is\({\rm{\$ 2500}}\). Let \({\rm{Y}}\) denote the amount of this individual's medical expenses paid by the insurance company. What is the expected value of\({\rm{Y}}\)?

(Hint: First figure out what value of \({\rm{X}}\) corresponds to the maximum out-of-pocket expense of \({\rm{\$ 2500}}\). Then write an expression for \({\rm{Y}}\) as a function of \({\rm{X}}\) (which involves several different pieces) and calculate the expected value of this function.)

The weekly demand for propane gas (in \({\rm{1000s}}\) of gallons) from a particular facility is an \({\rm{rv}}\) \({\rm{X}}\) with pdf

\({\rm{f(x) = }}\left\{ {\begin{array}{*{20}{c}}{{\rm{2}}\left( {{\rm{1 - }}\frac{{\rm{1}}}{{{{\rm{x}}^{\rm{2}}}}}} \right)}&{{\rm{1}} \le {\rm{x}} \le {\rm{2}}}\\{\rm{0}}&{{\rm{ otherwise }}}\end{array}} \right.\)

a. Compute the cdf of \({\rm{X}}\).

b. Obtain an expression for the \({\rm{(100p)th}}\) percentile. What is the value of \({\rm{\tilde \mu }}\)?

c. Compute \({\rm{E(X)}}\) and \({\rm{V(X)}}\).

d. If \({\rm{1}}{\rm{.5}}\) thousand gallons are in stock at the beginning of the week and no new supply is due in during the week, how much of the \({\rm{1}}{\rm{.5}}\) thousand gallons is expected to be left at the end of the week? (Hint: Let \({\rm{h(x) = }}\) amount left when demand \({\rm{ = x}}\).)

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.