Book Image

DAX Cookbook

By : Greg Deckler
Book Image

DAX Cookbook

By: Greg Deckler

Overview of this book

DAX provides an extra edge by extracting key information from the data that is already present in your model. Filled with examples of practical, real-world calculations geared toward business metrics and key performance indicators, this cookbook features solutions that you can apply for your own business analysis needs. You'll learn to write various DAX expressions and functions to understand how DAX queries work. The book also covers sections on dates, time, and duration to help you deal with working days, time zones, and shifts. You'll then discover how to manipulate text and numbers to create dynamic titles and ranks, and deal with measure totals. Later, you'll explore common business metrics for finance, customers, employees, and projects. The book will also show you how to implement common industry metrics such as days of supply, mean time between failure, order cycle time and overall equipment effectiveness. In the concluding chapters, you'll learn to apply statistical formulas for covariance, kurtosis, and skewness. Finally, you'll explore advanced DAX patterns for interpolation, inverse aggregators, inverse slicers, and even forecasting with a deseasonalized correlation coefficient. By the end of this book, you'll have the skills you need to use DAX's functionality and flexibility in business intelligence and data analytics.
Table of Contents (15 chapters)

Determining Pearson's coefficient of skewness

Skewness measures the asymmetry of the probability distribution compared with the normal distribution. In other words, compared to a normal distribution (bell curve), is the data skewed to one side or the other in terms of having a longer tail to the right or left? While we must be careful when interpreting skewness, a positive skew generally means that the tail is on the right, while negative skew indicates that the tail is on the left. This is compared to a normal distribution with two equal tails.

This recipe demonstrates how to calculate Karl Pearson's coefficient of skewness (the second method), which is given by the following formula:

In plain English, to find the skewness you subtract the median (the middle value) from the mean (average), multiply by three, and then divide by the standard deviation.