#### 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

## Hypotheses about the mean

In order to check a sample's parameter comparability with certain values, the following hypotheses are suggested: main (zero) hypothesis—when an assigned characteristic is commensurate with this quantity (for example, the mean is 5), and the alternative hypothesis, which differs from the zero hypothesis (for example, the mean is not equal to 5). When we are dealing with a sample of random variable observations, we don't know anything for sure, but we know to a certain degree of probability that this is a degree to which the null hypothesis can be either accepted or rejected. To test a hypothesis, there are functions that calculate the probability of the hypothesis. The more tests there are that give positive results with respect to one hypothesis, the more confidence there is in its truthfulness.

Hypotheses about a mean are checked using the `LocationTest` function. Let's generate a random sample of 1000 values with a normal distribution and test the hypothesis that...