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

English | 2021 | ISBN: 978-1119578147 | 304 Pages | PDF, EPUB | 32 MB

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.

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