#### 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)
Preface
Thinking in DAX
Tangling with Time and Duration
Transforming Text and Numbers
Evaluating Employment Measures
Processing Project Performance
Calculating Common Industry Metrics
Solving Statistical and Mathematical Formulas
Applying Advanced DAX Patterns
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# Approximating the area under a curve

Using polygons to approximate the area of curved shapes is a technique that has been known since ancient times. The concept is simple: draw polygons such as triangles or rectangles within the confines of a curved shape, calculate the area of those triangles and polygons, and you have approximated the area of a curved shape or under a curved line. Today, the technique is called a Riemann sum and is named after Bernhard Riemann, a German mathematician from the nineteenth century.

This recipe provides a method of approximating the area under a curve based upon the Riemann sum technique.

# Getting ready

To prepare for this recipe, do the following:

1. Open Power BI Desktop.
2. Create a table called...