#### Overview of this book

There are many algorithms for data analysis and it’s not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis. If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure. With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems. With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.
Table of Contents (15 chapters)
Mathematica Data Analysis
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
First Steps in Data Analysis
Broad Capabilities for Data Import
Creating an Interface for an External Program
Analyzing Data with the Help of Mathematica
Discovering the Advanced Capabilities of Time Series
Statistical Hypothesis Testing in Two Clicks
Predicting the Dataset Behavior
Rock-Paper-Scissors – Intelligent Processing of Datasets
Index

## Hypotheses on true sample distribution

In order to forecast the behavior of the data sample, you need to know not only its parameters, such as mean and variance, but also the distribution law, which controls the data. There are many distribution laws, and to suggest a hypothesis on similarity, you need to know the unique characteristics of each distribution. It is often sufficient to study a sample histogram to make a choice.

Using the `DistributionFitTest` function, you can test the hypothesis that the dataset was drawn from a population with a distribution, and the alternative hypothesis HA that it was not.

In order to check the main hypothesis, the data sample is tested. This tests the mean assessment of the difference d(x) of the empirical value of the distribution function and its predicted value, F(x). The following tests are conducted for univariate or multivariate distributions:

Test's name

Type of test

Description

"AndersonDarling"

Distribution, data

This is based on Expectation...