Create Table for Bell Curve

Shelton_Thompson

I am trying to use alteryx to create a table for a bell curve visual in PowerBI because PowerBI does not have a dynamic table capability (yet). 

My initial data is the following (just a sample)

Request ID	Total Time Taken	FY	Created Date
1	5	18/19	07/18/2019
2	10	18/19	07/01/2019
3	20	17/18	08/01/2017
4	30	17/18	01/01/2018

Using the summarize tool, I have performed the following actions on the data:

• Group by FY
• Min of Created Date
• Max of Created Date
• AvgNo0 of Total Time Taken
• StdDevNo0 of Total Time Taken
• Min  of Total Time Taken
• Max  of Total Time Taken

I then use a formula tool to create the x-3a and x+3a values (i.e. the min and max's of the array for the bell curve) 'x' being the mean and 'a' being the standard deviation.

I then use a generate rows tool to generate the array ranges for each fiscal year. 

This results in the following which is what I want. 

FY	Min_Created Date-Date Only	Max_Created Date-Date Only	Mean	Standard Dev	Min_Total Time (Business Days)	Max_Total Time (Business Days)	X-3A	X+3a	Value
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-18
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-17
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-16
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-15
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-14
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-13
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-12
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-11
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-10
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-9
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-8
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-7
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-6
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-5
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-4
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-3
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-2
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	-1
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	0
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	1
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	2
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	3
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	4
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	5
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	6
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	7
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	8
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	9
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	10
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	11
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	12
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	13
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	14
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	15
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	16
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	17
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	18
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	19
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	20
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	21
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	22
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	23
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	24
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	25
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	26
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	27
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	28
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	29
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	30
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	31
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	32
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	33
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	34
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	35
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	36
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	37
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	38
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	39
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	40
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	41
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	42
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	43
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	44
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	45
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	46
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	47
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	48
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	49
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	50
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	51
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	52
16/17	10/3/2016	5/31/2017	17	12	0.01	113.1	-18	53	53
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-10
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-9
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-8
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-7
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-6
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-5
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-4
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-3
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-2
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	-1
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	0
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	1
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	2
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	3
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	4
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	5
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	6
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	7
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	8
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	9
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	10
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	11
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	12
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	13
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	14
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	15
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	16
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	17
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	18
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	19
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	20
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	21
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	22
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	23
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	24
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	25
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	26
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	27
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	28
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	29
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	30
17/18	6/1/2017	5/31/2018	10	7	0.19	64.47	-10	31	31
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-10
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-9
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-8
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-7
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-6
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-5
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-4
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-3
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-2
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	-1
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	0
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	1
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	2
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	3
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	4
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	5
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	6
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	7
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	8
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	9
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	10
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	11
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	12
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	13
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	14
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	15
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	16
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	17
18/19	6/1/2018	5/31/2019	4	5	0.01	49.59	-10	18	18
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	-6
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	-5
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	-4
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	-3
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	-2
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	-1
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	0
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	1
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	2
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	3
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	4
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	5
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	6
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	7
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	8
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	9
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	10
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	11
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	12
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	13
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	14
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	15
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	16
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	17
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	18
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	19
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	20
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	21
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	22
19/20	6/2/2019	7/10/2019	8	5	0.02	24.56	-6	23	23

Next, I need to do the following and this is where I am stuck trying to figure out how to do this. 

1. Calculate the Norm Distribution for each array value. In excel this is =NORM.DIST([ARRAYVALUE],[MEAN],[STND DEV],FALSE)
2. Calculate the ranges within the bell curve array for the Total Time Taken 
3. Count how many requests fall into each region and then calculate a percentage

Ultimately in PowerBI, I want to end up with something like the following but also include labels of the Total Time Taken and the counts and percentages but in order to do this, I need to feed PowerBI the table or tables.

Open to any suggestions on how to do this or if potentially there is a better way to do this...

Shelton_Thompson

Alright I got it! I took it a step further and also created a Macro so no one else has to try to figure this out with a built-in Standard Normalization Table. 

https://gallery.alteryx.com/#!app/Bell%2BCurve/5d3739e0826fd3108052ad22