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商品詳細
Applied Univariate, Bivariate, and Multivariate Statistics Using Python: A Beginner's Guide to Advanced Data Analysis. Pythonを用いた応用一変量・ 二変量・多変量統計学
・ISBN 978-1-119-57814-7 hard US$ 132.95
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| 著者・編者 | Denis, Daniel J., |
|---|---|
| 出版社 | (Wiley, US) |
| 出版年月 | 2021.07 |
| ページ数 | 304 pp. |
| 言語 | ENG |
| ニュース番号 | ＜662-296 662-P1050＞ |
解説
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.