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)

Using Kaplan-Meier survival curves

The Kaplan-Meier estimator, also known as the product-limit estimator, is a statistical measure used to estimate the percentage chance of survival of a population over a given length of time. The formula for the Kaplan-Meier estimator is given as follows:

In plain English, this formula means that the survivability at any time (t) is the product of 1 minus the number of end events (d), the non-surviving population, divided by the number of the population that have not reached an end event (n), the surviving population, for all increments of time (t) that are less than or equal to the current time (t).

Think of this as essentially a running product over time. This means that values for the function are calculated for each increment of time (t) less than or equal to the present time and then multiplied together to get a new value. Obviously, this...