#### 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.
Mathematica Data Analysis
Credits
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

## Summary

In this chapter, we learned how to test hypotheses on possible parameters of a sample and also received evidence that this task does not take much time. We learned that in order to estimate a sample mean, we can use the `LocationTest` function, and with its help, we can test hypotheses on the equality of the means of two samples. We also acquired a skill to test hypotheses on the true value of a variance with the help of the `VarianceTest` function. We found out that we can check the degree of dependence of data samples using the `CorrelationTest` function. In the end, we learned how to test hypotheses on the true distribution of a sample with the help of the `DistributionFitTest` function. In the next chapter, we will learn how to predict data using all the knowledge we've gained so far.