In biofiltration of wastewater, air discharged from a treatment facility is passed through a damp porous membrane that causes contaminants to dissolve in water and be transformed into harmless products. Air discharge usually contains hydrogen sulfide, organic sulfides, and other volatile organic compounds (VOCS). For this experiment, it is desired to investigate the relationship between air inlet temperature and removal efficiency of porous membrane. The accompanying data on x = inlet temperature (°C) and y = removal efficiency (%) is shown below. Removal Removal Observation Temp Observation Temp 1 7.68 98.09 17 8.55 98.27 6.51 98.25 18 7.59 98 3 6.49 97.82 19 6.94 98.09 4 5.48 97.82 20 8.31 98.25 6.57 97.82 21 10.5 98.41 10.13 97.93 22 16.02 98.51 7 15.71 98.38 23 17.83 98.71 8. 16.77 98.89 24 17.03 98.79 9. 17.13 98.96 25 16.18 98.87 10 17.63 98.9 26 16.31 98.76 11 16.72 98.68 27 14.44 98.58 12 15.45 98.69 28 12.78 98.73 13 12.21 98.51 29 12.25 98.45 14 11.44 98.09 30 11.64 98.37 15 10.17 98.25 31 11.34 98.36 16 9.64 98.36 32 10.97 98.45 Using the data in Experiment 1, determine if there is sufficient evidence that inlet temperature could improve the removal efficiency of the membrane. a. How does the inlet temperature affect the removal rate? b. How strong is the relationship you concluded in 1a? c. How significant is the relationship in 1a? d. To a certain degree, can you estimate the expected removal rate with inlet temperature? Demonstrate this using an example. What is the typical deviation from the estimate? e. Using residual analysis, verify that your conclusion in 1a-1c and your estimate in 1d are valid. f. support your residual analysis in 1e by checking the normality of the errors in estimates.

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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Hello! I need answer for letter F only using Anderson-Darling test to support the normal distribution of the data.

In biofiltration of wastewater, air discharged from a treatment facility is passed through a damp porous membrane that causes contaminants to
dissolve in water and be transformed into harmless products. Air discharge usually contains hydrogen sulfide, organic sulfides, and other volatile
organic compounds (VOCS).
For this experiment, it is desired to investigate the relationship between air inlet temperature and removal efficiency of porous membrane. The
accompanying data on x = inlet temperature (°C) and y = removal efficiency (%) is shown below.
Removal
Removal
Observation Temp
Observation Temp
%
%
1
7.68
98.09
17
8.55
98.27
6.51
98.25
18
7.59
98
3
6.49
97.82
19
6.94
98.09
4
5.48
97.82
20
8.31
98.25
5
6.57
97.82
21
10.5
98.41
10.13
97.93
22
16.02 98.51
7
15.71
98.38
23
17.83 98.71
8
16.77 98.89
24
17.03 98.79
9.
17.13
98.96
25
16.18 98.87
10
17.63
98.9
26
16.31
98.76
11
16.72 98.68
27
14.44 98.58
12
15.45 98.69
28
12.78 98.73
13
12.21
98.51
29
12.25 98.45
14
11.44 98.09
30
11.64 98.37
15
10.17
98.25
31
11.34 98.36
16
9.64
98.36
32
10.97 98.45
Using the data in Experiment 1, determine if there is sufficient evidence that inlet temperature could improve the removal efficiency of the
membrane.
a. How does the inlet temperature affect the removal rate?
b. How strong is the relationship you concluded in 1a?
c. How significant is the relationship in 1a?
d. To a certain degree, can you estimate the expected removal rate with inlet temperature? Demonstrate this using an example. What is the
typical deviation from the estimate?
e. Using residual analysis, verify that your conclusion in 1a-1c and your estimate in 1d are valid.
f. support your residual analysis in 1e by checking the normality of the errors in estimates.

Features Features Normal distribution is characterized by two parameters, mean (µ) and standard deviation (σ). When graphed, the mean represents the center of the bell curve and the graph is perfectly symmetric about the center. The mean, median, and mode are all equal for a normal distribution. The standard deviation measures the data's spread from the center. The higher the standard deviation, the more the data is spread out and the flatter the bell curve looks. Variance is another commonly used measure of the spread of the distribution and is equal to the square of the standard deviation.

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