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

Applied Univariate, Bivariate, and Multivariate Statistics Using Python

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.

Most of the datasets used  in the book are small enough to be easily entered into Python manually,  though they can also be downloaded for free from www.datapsyc.com.  Only minimal knowledge of statistics is assumed, making the book 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  review of essential statistical principles, including types of data,  measurement, significance tests, significance levels, and type I and  type II errors
• An introduction to Python, exploring how to communicate with Python
• A  treatment of exploratory data analysis, basic statistics and  visual displays, including frequencies and descriptives, q-q plots,  box-and-whisker plots, and data management
• An  introduction to topics such as ANOVA, MANOVA and discriminant analysis,  regression, principal components analysis, factor analysis, cluster  analysis, among others, exploring the nature of what these techniques  can vs. cannot do on a methodological level

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.