Applied Univariate, Bivariate, and Multivariate Statistics Using Python: A Beginner’s Guide to Advanced Data Analysis

Book Description

A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python 

Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied.

The datasets used in the book are small enough to easily be entered into Python manually, although they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, and the book is perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python.
Readers will also benefit from the inclusion of:

• A thorough review of essential statistical principles, including types of data, scales of measurement, significance tests, significance levels, and type I and type II errors
• An introduction to Python, including how to communicate with Python
• A treatment of exploratory data analysis, basic statistics, and visual displays, including frequencies and descriptives, stem-and-leaf plots, q-q plots, box-and-whisker plots, and data transformations
• An exploration of data management in Python

Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.

中文：

书名：使用Python应用单变量、双变量和多变量统计学：高级数据分析初学者指南

这是一本实用的《How-to》参考资料，适用于任何人在Python中执行基本的统计分析和数据管理任务。

使用Python应用单变量、双变量和多变量统计它在一个单一的一站式参考中提供了对使用Python执行的各种统计方法的全面介绍。这本书包含了用户友好的指导和说明，如何使用Python运行各种统计过程，而不会陷入不必要的理论泥潭。在整个过程中，作者强调了一套用于发现经验模式的计算工具，以及几个可以立即应用的流行的统计分析和数据管理任务。

书中使用的数据集足够小，可以很容易地手动输入到Python中，尽管也可以从www.datapsyc.com免费下载。这本书只假定对统计学有很少的了解，对于那些寻求使用Python进行统计分析的易于访问的工具包的人来说，这本书是完美的。使用Python应用单变量、双变量和多变量统计这代表了学习如何使用Python分析数据的最快方式。
读者还将受益于以下内容：

• 全面回顾基本统计原理，包括数据类型、测量范围、显著性检验、显著性水平以及第一类和第二类错误
• 关于Python的简介，包括如何与Python通信
• 对探索性数据分析、基本统计和可视化显示的处理，包括频率和描述、茎叶图、Q-Q图、盒子和胡须图以及数据转换
• 在Python中实现数据管理的探索

非常适合社会、行为和自然科学领域的本科生和研究生。使用Python应用单变量、双变量和多变量统计还将在寻求在Python中进行单变量、双变量和多变量分析的快速首选资源的研究人员和数据分析师的库中获得一席之地。