1. You would like to estimate bacterial levels (specifically Escherichia coli) for a large shallow pond. You collect 1-L water samples from 5 random locations in the pond. You measure E. coli levels using 10-mL subsamples (three per 1-L water sample). (a) This sampling design would be an example of what type of sampling strategy? (b) Table 1 shows the data that you collected. Using equations appropriate to your sampling strategy, calculate the mean E. coli concentration for the pond and estimate the standard error. .3.. 1x107 I 10.000
1. You would like to estimate bacterial levels (specifically Escherichia coli) for a large shallow pond. You collect 1-L water samples from 5 random locations in the pond. You measure E. coli levels using 10-mL subsamples (three per 1-L water sample). (a) This sampling design would be an example of what type of sampling strategy? (b) Table 1 shows the data that you collected. Using equations appropriate to your sampling strategy, calculate the mean E. coli concentration for the pond and estimate the standard error. .3.. 1x107 I 10.000
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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![1. You would like to estimate bacterial levels (specifically Escherichia coli) for a large shallow
pond. You collect 1-L water samples from 5 random locations in the pond. You measure E. coli
levels using 10-mL subsamples (three per 1-L water sample).
(a) This sampling design would be an example of what type of sampling strategy?
(b) Table 1 shows the data that you collected. Using equations appropriate to your sampling
strategy, calculate the mean E. coli concentration for the pond and estimate the standard error.
The estimated volume of the pond is 10,000 m³ or 1×10' L, such that the total number of
possible sample units is N= pond volume / sample volume = 1×107 L /1 L = 1×107. (Note,
that fi = n/N is very small!)
Table 1. Measured E. coli levels (in CFU/100 mL) in sample subsamples.
Also indicated are the sample means (y;) and sample sum-of-squares
(SSy; i.e. E(yy – y.)³,.
Recalculate the
(c)
mean, treating each
subsample as a sample
(i.e. Eyi /15, where
2yi is the sum of all
measured values). How
does this mean compare
Subsamples
Sy
2,400
Sample
1
2
3
Yi
1
20
80
80
60.0
50
270
50
123.3
32,300
3
120
170
96.7
15,300
4
70
23.3
3,270
5
110
40
30
60.0
3,800
to the mean you
calculated in (b)?
(d) Assume one of the subsamples was lost (so unequal number subsamples across the samples)
and you recalculated the mean as in (b) and (c). How might you expect the two mean
calculations to compare? Which version should be a better estimate of the true population
mean? Why? [hint: try removing subsample 1 of sample 4]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fcd617b0d-2424-4a34-8571-67fe540a0678%2F318cac52-8936-480a-b98d-1087b4240770%2Fqumgw1a_processed.png&w=3840&q=75)
Transcribed Image Text:1. You would like to estimate bacterial levels (specifically Escherichia coli) for a large shallow
pond. You collect 1-L water samples from 5 random locations in the pond. You measure E. coli
levels using 10-mL subsamples (three per 1-L water sample).
(a) This sampling design would be an example of what type of sampling strategy?
(b) Table 1 shows the data that you collected. Using equations appropriate to your sampling
strategy, calculate the mean E. coli concentration for the pond and estimate the standard error.
The estimated volume of the pond is 10,000 m³ or 1×10' L, such that the total number of
possible sample units is N= pond volume / sample volume = 1×107 L /1 L = 1×107. (Note,
that fi = n/N is very small!)
Table 1. Measured E. coli levels (in CFU/100 mL) in sample subsamples.
Also indicated are the sample means (y;) and sample sum-of-squares
(SSy; i.e. E(yy – y.)³,.
Recalculate the
(c)
mean, treating each
subsample as a sample
(i.e. Eyi /15, where
2yi is the sum of all
measured values). How
does this mean compare
Subsamples
Sy
2,400
Sample
1
2
3
Yi
1
20
80
80
60.0
50
270
50
123.3
32,300
3
120
170
96.7
15,300
4
70
23.3
3,270
5
110
40
30
60.0
3,800
to the mean you
calculated in (b)?
(d) Assume one of the subsamples was lost (so unequal number subsamples across the samples)
and you recalculated the mean as in (b) and (c). How might you expect the two mean
calculations to compare? Which version should be a better estimate of the true population
mean? Why? [hint: try removing subsample 1 of sample 4]
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