#### 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
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

## Classical predicting

In the previous chapters, we became familiar with data samples and time series and got to know how to define their parameters, which means we were able to predict future values. As a matter of fact, this is classical prediction. However, if the statistical tools seems a bit difficult for you and there is no time to gain an understanding, you can use a quicker solution—the `Predict` function. After receiving an input data array, it immediately issues a predicted value by keeping all the calculations behind the scenes:

In this case, we took a preliminary dataset—note the list entry in the format: input data -> value. Then, using the `Predict` function, we obtained `PredictorFunction` that can output any prediction value depending on the input data. For example, if the input value is equal to 4, the output will be 5.47. After reviewing our data, Mathematica came to the conclusion that the best model for prediction is linear regression. With the graph that we have built by successively...