{"product_id":"applied-univariate-bivariate-and-multivariate-statistics-using-python-a-beginners-guide-to-advanced-data-analysis-hardcover","title":"Applied Univariate, Bivariate, and Multivariate Statistics Using Python: A Beginner's Guide to Advanced Data Analysis - Hardcover","description":"\u003cp\u003eby \u003cb\u003eDaniel J. Denis\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003cb\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using Python\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA practical, \"how-to\" reference for anyone performing essential statistical analyses and data management tasks in Python\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using Python\u003c\/i\u003e 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.\u003c\/p\u003e\u003cp\u003eMost 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. \u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using Python\u003c\/i\u003e represents the fastest way to learn how to analyze data with Python.\u003c\/p\u003e\u003cp\u003eReaders will also benefit from the inclusion of: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors\u003c\/li\u003e\n\u003cli\u003eAn introduction to Python, exploring how to communicate with Python\u003c\/li\u003e\n\u003cli\u003eA treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management\u003c\/li\u003e\n\u003cli\u003eAn 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\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePerfect for undergraduate and graduate students in the social, behavioral, and natural sciences, \u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using Python\u003c\/i\u003e 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.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA practical, \"how-to\" reference for anyone performing essential statistical analyses and data management tasks in Python\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using Python\u003c\/i\u003e 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.\u003c\/p\u003e\u003cp\u003eMost 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. \u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using Python\u003c\/i\u003e represents the fastest way to learn how to analyze data with Python.\u003c\/p\u003e\u003cp\u003eReaders will also benefit from the inclusion of: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors\u003c\/li\u003e\n\u003cli\u003eAn introduction to Python, exploring how to communicate with Python\u003c\/li\u003e\n\u003cli\u003eA treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management\u003c\/li\u003e\n\u003cli\u003eAn 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\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePerfect for undergraduate and graduate students in the social, behavioral, and natural sciences, \u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using Python\u003c\/i\u003e 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.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDaniel J. Denis, PhD, \u003c\/b\u003e is Professor of Quantitative Psychology at the University of Montana. He is author of \u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics\u003c\/i\u003e and \u003ci\u003eApplied Univariate, Bivariate, and Multivariate Statistics Using R\u003c\/i\u003e.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 304\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.69 x 10 x 7 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 11, 2021\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42709185003583,"sku":"9781119578147","price":222.83,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/3fecde96caab85f582ee3db0b768c004.webp?v=1765050489","url":"https:\/\/dhlswag.com\/products\/applied-univariate-bivariate-and-multivariate-statistics-using-python-a-beginners-guide-to-advanced-data-analysis-hardcover","provider":"BBB","version":"1.0","type":"link"}