Percentile of a table stored in a variable
Hi all, I would like to detect outliers in my daily data report by using the percentile function. I have a detail table (table 1) and I want to calculate the percentile of the second table which shows the tickets per day. If I create a fixed calculated table I have to add "ALL / AllExcept" function inside Percentile function: Percentile = PERCENTILEX.EXC (ALL(TicketsByDate), TicketsByDate[Count_Tickets], .90 ) I need to apply external filters and I don't want to rely on the All/AllExcept function. For this reason, I want to store my calculated table in a variable. The problem is that I can't calculate the percentile of this calculated table in a variable. I get the following error: "If k is not a multiple of 1/(n + 1), PERCENTILEX.EXC will interpolate to determine the value at the k-th percentile." Can someone help me? Thanks!!!Outlier Detection Logic shows duplicated values
Hello, I'm trying to identify the outliers in a coworker's table. The table contains the number of calls a coworker accepted and the time he spent on the call (Handling time). I followed this video: https://www.youtube.com/watch?v=RoglNZ2dXW8&t=22s&ab_channel=EnterpriseDNA and built my Outlier Detection Logic. I defined my outlier criteria in there. The problem is, once I add the scatter plot all coworkers appear as non-outliers and outliers at the same time, I created a table to see what's happening and I also saw the coworkers duplicated as outliers and non-outliers. I've created a sample of the project, the PBXI sample data can be found here: https://we.tl/t-qsilTM42sN Any help or tips would be highly appreciated.
Change axis labels in visualization Detect Outliers
Hi, I create scatter plot which detect outliers (with custom visualization Detect Outliers). I need to make time series, where on x axis is months and on y axis are costs in these months. But when I use this visualization, then I have on x axis only numbers of observations and label "order" and not "month" as I wished. Can anyone helps me please?
