Permeability of sandstone during weathering.Refer to the Geographical Analysis(Vol. 42, 2010) study of the decay properties of sandstone when exposed to the weather, Exercises 2.47 and 2.65 (pp. 96 and 104). Recall that slices of sandstone blocks were measured for permeability under three conditions: no exposure to any type of weathering (A), repeatedly sprayed with a 10% salt solution (B), and soaked in a 10% salt solution and dried (C).

a.Combine the mean (from Exercise 2.47) and standard deviation (from Exercise 2.65) to make a statement about where most of the permeability measurements for Group A sandstone slices will fall. Which rule did you use to make this inference and why?

b.Repeat part afor Group B sandstone slices.

c.Repeat part afor Group C sandstone slices.

d.Based on all your analyses, which type of weathering (type A, B, or C) appears to result in faster decay (i.e., higher permeability measurements)?

Short Answer

Expert verified

(a) 3 standard deviations from mean

(b) 1 standard deviations from mean

(c) 2 standard deviations from mean

(d) Type B

Step by step solution

01

Finding the standard deviation from mean where most of the permeability measurements fall

Mean from Ex. 47 = 73.62

Standard Deviation from Ex. 65 = 14.48

Minimum = 55.20

Maximum = 122.40

To find the standard deviation where most of the permeability measurements will fall, we will first find k.

x¯-ks=Max73.62-k(14.48)=122.40k(14.48)=122.40-73.62k(14.48)=48.78k=48.7814.48k=3.37

Therefore, we can say that most of the permeability measurements will fall between 3 standard deviations from mean.

We have used the Chebyshev Rule, because we did not have any information about the distribution of the data. And Chebyshev’s rule allows us to use it on any type of distribution.

02

Computing the standard deviation from mean where most of the permeability measurements fall

Mean from Ex. 47 = 128.54

Standard Deviation from Ex. 65 = 21.97

Minimum = 50.40

Maximum = 150.00

To find the standard deviation where most of the permeability measurements will fall, we will first find k.

x¯-ks=Max128.54-k(21.97)=150k(21.97)=150-128.54k(21.97)=21.46k=21.4621.97k=0.97

Therefore, we can say that most of the permeability measurements for condition 2 will fall approximately between1 standard deviations from mean.

03

Calculating the standard deviation from mean where most of the permeability measurements fall

Mean from Ex. 47 = 83.07

Standard Deviation from Ex. 65 = 20.05

Minimum = 52.20

Maximum = 129.00

To find the standard deviation where most of the permeability measurements will fall, we will first find k.

x¯-ks=Max83.07-k(20.05)=129k(20.05)=129-83.07k(20.05)=45.93k=45.9320.05k=2.29

Therefore, we can say that most of the permeability measurements for condition 3 will fall between 2 standard deviations from mean.

04

Identifying the group that decays fast

Sandstone decays faster under Type B weathering, because most of the values in this group fall between 1 standard deviation from mean.

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Most popular questions from this chapter

Monitoring weights of flour bags.When it is working properly, a machine that fills 25-pound bags of flour dispenses an average of 25 pounds per fill; the standard deviation of the amount of fill is .1 pound. To monitor the performance of the machine, an inspector weighs the contents of a bag coming off the machine’s conveyor belt every half hour during the day. If the contents of two consecutive bags fall more than 2 standard deviations from the mean (using the mean and standard deviation given above), the filling process is said to be out of control, and the machine is shut down briefly for adjustments. The data

given in the following table are the weights measured by the inspector yesterday. Assume the machine is never shut down for more than 15 minutes at a time. At what times yesterday was the process shut down for adjustment? Justify your answer.

Time

Weight (pounds)

8:00 a.m

25.10

8:30

25.15

9:00

24.81

9:30

24.75

10:00

25.00

10:30

25.05

11:00

25.23

11:30

25.25

12:00

25.01

12:30 p.m

25.06

1:00

24.95

1:30

24.80

2:00

24.95

2:30

25.21

3:00

24.90

3:30

24.71

4:00

25.31

4:30

25.15

5:00

25.20

Question: Hourly road accidents in India.An analysis of road accident data in India was undertaken, and the results were published in the Journal of Big Data(Vol. 2, 2015). For a particular cluster of roads, the hourly numbers of accidents totaled over a recent 5-year period are listed in the next table. (These results are adapted from a figure in the journal article.) Create a scatterplot of the data, with a number of accidents on the vertical axis and hours on the horizontal axis. What type of trend (if any) do you detect in the data?

Hour

Number

1

2

3

4

5

6

7

8

9

10

11

12

125

148

159

270

281

295

302

216

225

353

400

327

Construct a relative frequency histogram for the data summarized in the accompanying table.

In terms of percentiles, define QL, QM and QU

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