How to transform and model nonnormal distribution JMP15

[sanchez_dan]

Hello,

I'm working with a dataset produced from a 5-variable full factorial screening with 3 centerpoints. The raw data is heavily skewed with an exponential distribution. I've tried log, log10, square root and various box-cox transformations and can't seem to get anything even nearly approaching a normal distribution. 

Condition	Pattern	data1
1	+−−+−	0.59
2	−+−−−	1.6
3	−−−+−	1.78
4	+−++−	0.45
5	+−−−−	1.37
6	−++−−	0.87
7	++−+−	0.05
8	−−−−−	4.46
9	++−−−	0.14
10	−+−+−	0.36
11	+++−−	0.11
12	+−+−−	0.8
13	−+++−	0.33
14	++++−	0.05
15	−−+−−	2.43
16	−−++−	1.53
17	0	0.86
18	0	0.79
19	0	0.9
20	−+−−+	0.94
21	+−+++	0.87
22	−−+++	0.91
23	−−−++	0.72
24	−+−++	0.05
25	−−+−+	2.74
26	−++−+	0.72
27	−++++	0.08
28	+−+−+	2.92
29	−−−−+	4.08
30	++−−+	0.88
31	+−−−+	3.98
32	+−−++	0.82
33	+++++	0.08
34	+++−+	0.78
35	++−++	0.06

1. What kind of transform is appropriate to handle the data set?

2. If there aren't any appropriate methods of transforming the data, how can it be modeled? (using Fit Model, etc)

[PaigeMiller · Accepted solution]

I think you would be better off asking in the JMP community.