An article in the Journal of Pharmaceuticals Sciences (Vol. 80, 1991, pp. 971-977) presents data on the observed mole fraction solubility of a solute at a constant temperature and the dispersion, dipolar, and hydrogen bonding Hansen partial solubility parameters. The data are as shown in the following table, where y is the negative logarithm of the mole fraction solubility, x1 is the dispersion partial solubility, x2 is the dipolar partial solubility, and x3 is the hydrogen bonding partial solubility. Using 1) Data Analysis Tool in Excel and 2) fitlm function in MATLAB: (a) Fit the model Y= B0+ Bx1+ B2x2+ B3x3 (b) Test hypothesis using alpha = 0.05 Observation Number X2 X3 %3D 1 0.22200 7.3 0.0 0.0 and state conclusion 0.39500 8.7 0.0 0.3
An article in the Journal of Pharmaceuticals Sciences (Vol. 80, 1991, pp. 971-977) presents data on the observed mole fraction solubility of a solute at a constant temperature and the dispersion, dipolar, and hydrogen bonding Hansen partial solubility parameters. The data are as shown in the following table, where y is the negative logarithm of the mole fraction solubility, x1 is the dispersion partial solubility, x2 is the dipolar partial solubility, and x3 is the hydrogen bonding partial solubility. Using 1) Data Analysis Tool in Excel and 2) fitlm function in MATLAB: (a) Fit the model Y= B0+ Bx1+ B2x2+ B3x3 (b) Test hypothesis using alpha = 0.05 Observation Number X2 X3 %3D 1 0.22200 7.3 0.0 0.0 and state conclusion 0.39500 8.7 0.0 0.3
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using matlab
![An article in the Journal of Pharmaceuticals Sciences (Vol. 80, 1991, pp. 971-977) presents data on the
observed mole fraction solubility of a solute at a constant temperature and the dispersion, dipolar, and
hydrogen bonding Hansen partial solubility parameters. The data are as shown in the following table,
where y is the negative logarithm of the mole fraction solubility, x1 is the dispersion partial solubility, x2
is the dipolar partial solubility, and x3 is the hydrogen bonding partial solubility. Using 1) Data Analysis
Tool in Excel and 2) fitlm function in MATLAB:
(a) Fit the model Y= BO+ Bx1+ B2x2+
B3x3
(b) Test hypothesis using alpha = 0.05
Observation
Number
X2
X3
1
0.22200
7.3
0.0
0.0
and state conclusion
2
0.39500
8.7
0.0
0.3
(c) Plot the data
3
0.42200
8.8
0.7
1.0
4
0.43700
8.1
4.0
0.2
5
0.42800
9.0
0.5
1.0
0.46700
8.7
1.5
2.8
7
0.44400
9.3
2.1
1.0
0.37800
7.6
5.1
3.4
9
0.49400
10.0
0.0
0.3
10
0.45600
8.4
3.7
4.1
11
0.45200
9.3
3.6
2.0
12
0.11200
7.7
2.8
7.1
13
0.43200
9.8
4.2
2.0
14
0.10100
7.3
2.5
6.8
15
0.23200
8.5
2.0
6.6
16
0.30600
9.5
2.5
5.0
17
0.09230
7.4
2.8
7.8
18
0.11600
7.8
2.8
7.7
19
0.07640
7.7
3.0
8.0
20
0.43900
10.3
1.7
4.2
21
0.09440
7.8
3.3
8.5
22
0.11700
7.1
3.9
6.6
23
0.07260
7.7
4.3
9.5
24
0.04120
7.4
6.0
10.9
25
0.25100
7.3
2.0
5.2
26
0.00002
7.6
7.8
20.7](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F49a93640-9ff9-47a9-8caa-fe0ed51c7fbf%2F89004337-eb75-4f69-a95b-f9d1a8b8c686%2F3m64fld_processed.jpeg&w=3840&q=75)
Transcribed Image Text:An article in the Journal of Pharmaceuticals Sciences (Vol. 80, 1991, pp. 971-977) presents data on the
observed mole fraction solubility of a solute at a constant temperature and the dispersion, dipolar, and
hydrogen bonding Hansen partial solubility parameters. The data are as shown in the following table,
where y is the negative logarithm of the mole fraction solubility, x1 is the dispersion partial solubility, x2
is the dipolar partial solubility, and x3 is the hydrogen bonding partial solubility. Using 1) Data Analysis
Tool in Excel and 2) fitlm function in MATLAB:
(a) Fit the model Y= BO+ Bx1+ B2x2+
B3x3
(b) Test hypothesis using alpha = 0.05
Observation
Number
X2
X3
1
0.22200
7.3
0.0
0.0
and state conclusion
2
0.39500
8.7
0.0
0.3
(c) Plot the data
3
0.42200
8.8
0.7
1.0
4
0.43700
8.1
4.0
0.2
5
0.42800
9.0
0.5
1.0
0.46700
8.7
1.5
2.8
7
0.44400
9.3
2.1
1.0
0.37800
7.6
5.1
3.4
9
0.49400
10.0
0.0
0.3
10
0.45600
8.4
3.7
4.1
11
0.45200
9.3
3.6
2.0
12
0.11200
7.7
2.8
7.1
13
0.43200
9.8
4.2
2.0
14
0.10100
7.3
2.5
6.8
15
0.23200
8.5
2.0
6.6
16
0.30600
9.5
2.5
5.0
17
0.09230
7.4
2.8
7.8
18
0.11600
7.8
2.8
7.7
19
0.07640
7.7
3.0
8.0
20
0.43900
10.3
1.7
4.2
21
0.09440
7.8
3.3
8.5
22
0.11700
7.1
3.9
6.6
23
0.07260
7.7
4.3
9.5
24
0.04120
7.4
6.0
10.9
25
0.25100
7.3
2.0
5.2
26
0.00002
7.6
7.8
20.7
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