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Modern Python Cookbook

Modern Python Cookbook

By : Steven F. Lott
4.8 (15)
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Modern Python Cookbook

Modern Python Cookbook

4.8 (15)
By: Steven F. Lott

Overview of this book

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand. This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications. The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it. By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.
Table of Contents (18 chapters)
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16
Other Books You May Enjoy
17
Index

Confirming that the data is random – the null hypothesis

One of the important statistical questions is framed as the null hypothesis and an alternate hypothesis about sets of data. Let's assume we have two sets of data, S1 and S2. We can form two kinds of hypothesis in relation to the data:

  • Null: Any differences are random effects and there are no significant differences.
  • Alternate: The differences are statistically significant. Generally, we consider that the likelihood of this happening stochastically to samples that only differ due to random effects must be less than 5% for us to deem the difference "statistically significant."

This recipe will show one of many ways in which to evaluate data to see whether it's truly random or whether there's some meaningful variation.

Getting ready

The rare individual with a strong background in statistics can leverage statistical theory to evaluate the standard deviations...

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