{"id":3347,"date":"2019-05-23T19:22:44","date_gmt":"2019-05-23T17:22:44","guid":{"rendered":"https:\/\/geekosas.com\/?p=3347"},"modified":"2026-05-23T19:23:07","modified_gmt":"2026-05-23T17:23:07","slug":"uncovering-averages","status":"publish","type":"post","link":"https:\/\/geekosas.com\/index.php\/2019\/05\/23\/uncovering-averages\/","title":{"rendered":"Uncovering averages"},"content":{"rendered":"

As they say, averages hide many things. In the article gender-pay-gap-en-tecnologia<\/a> we saw an analysis that showed how, for that data, the salary difference between men and women can be explained by factors other than gender.<\/p>\n

Now we are going to look at a technique, based on machine learning, that is very simple to explain and communicate to "uncover" what lies beneath the averages.<\/p>\n

Approach<\/h4>\n

Imagine you are the data scientist in the satisfaction area of a company and you are in charge of maintaining the rating your customers give to the company’s service (or some other KPI). This rating is obtained through a monthly sampling of customers who contacted the call center.<\/p>\n

Today is the day, the new satisfaction survey has arrived, and your boss is eager to know how the work was done last month and Eureka! The average rating increased from 5.577 to 5.723, so everyone gets the bonus and goes out to lunch.<\/p>\n

But what does that average hide? Did the rating really increase? Let’s see how to quickly perform this analysis.<\/p>\n

Data<\/h4>\n

For each month (previous and current) we have a table with 2000 observations that looks like this (simulated data):<\/p>\n

id        causa genero region nota\n1  1       equipo hombre  norte    6\n2  2        saldo  mujer  norte    8\n3  3  facturacion hombre  norte    2\n4  4        saldo  mujer centro    6\n5  5 conectividad  mujer centro    9\n6  6 conectividad hombre centro    4<\/code><\/pre>\n
Model<\/h4>\n
To understand the variables that explain the rating, we will calibrate a tree using rpart that looks like this:<\/p>\n
library(rpart)\nlibrary(rattle)\nfit = rpart(nota ~ causa + genero + region,data1,cp = 0.015)\nfancyRpartPlot(fit)<\/code><\/pre>\n\n
\"\"<\/figure>\n\n
Basically it reads like this: In the first branch (top), the average is 5.6, but if we open that branch according to the reason for the call, when the reason is connectivity or billing, the rating drops to 4.4, otherwise the rating rises to 6.3.<\/p>\n
Each of the previous branches opens again: the left branch by gender (of the customer), where men give a rating of 5.1 and women 5.2, while the right branch opens by the geographic area where the customer lives.<\/p>\n
The intuition is the following: my total rating can change for 2 reasons:<\/p>\n