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.

Autorentext

Daniel J. Denis, PhD, is Professor of Quantitative Psychology at the University of Montana. He is author of Applied Univariate, Bivariate, and Multivariate Statistics and Applied Univariate, Bivariate, and Multivariate Statistics Using R.

Klappentext

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.

Zusammenfassung
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 levelPerfect 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.

Inhalt
Preface xii

1 A Brief Introduction and Overview of Applied Statistics 1

1.1 How Statistical Inference Works 4

1.2 Statistics and Decision-Making 7

1.3 Quantifying Error Rates in Decision-Making: Type I and Type II Errors 8

1.4 Estimation of Parameters 9

1.5 Essential Philosophical Principles for Applied Statistics 11

1.6 Continuous vs. Discrete Variables 13

1.6.1 Continuity Is Not Always Clear-Cut 15

1.7 Using Abstract Systems to Describe Physical Phenomena:

Understanding Numerical vs. Physical Differences 16

1.8 Data Analysis, Data Science, Machine Learning, Big Data 18