# Tags: RANKX

This post is an extension to the one I posted here – Segment towns according to volume contribution and market share. Here’s a simple dataset of Shampoo sales in the state of Rajasthan, India. For a chosen segment, one may want to segment the 4 towns based on the following conditions: Based on the two screenshots […]

Relative size factor (RSF) is a test to identify anomalies where the largest amount for subsets in a given key is outside the norm for those subsets. This test compares the top two amounts for each subset and calculates the RSF for each. In order to identify potential fraudulent activities in invoice payment data, one […]

Visualise a 5 column dataset as show below.  This is a very small sample of the actual dataset.  It shows the date on which supplies were received for each item from Vendors and whether those supplies had errors in them.  Finally those identified errors have been bucketed into relevant categories.  The Item ID# is a […]

Here is a simple 3 column dataset showing Categories, Date and Value Catagorie Date Value Fish 08-12-2015 6 Crab 05-12-2015 7 Crab 04-12-2015 6 Bird 27-11-2015 4 Snow 25-11-2015 10 Cat 21-11-2015 7 Dog 12-11-2015 5 Dog 28-10-2015 5 Fish 12-10-2015 3 Bird 11-10-2015 9 Dog 22-09-2015 9 Crab 17-08-2015 8 Cat 11-08-2015 1 Fish […]

Imagine a two column dataset – Customer Code and Material Number (with alphanumeric data).  The objective is to determine the second highest quantity sold per Customer code. Since we will first have to determine the Customer wise and Material Number wise quantity sold, a good way to get started is to use a Pivot Table.  […]

Assume a five column dataset – ID, Age, Gender, Time and Class.  For chosen ID’s, the objective is to: 1. Assign a Rank (in ascending order of time i.e. lowest time will be rank 1 and so on) to each ID 2. Determine the overall place of each ID – Count of unique time entries […]