Daniel J. Denis

Applied Univariate, Bivariate, and Multivariate Statistics Using Python (eBook, ePUB)

A Beginner's Guide to Advanced Data Analysis

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 304
• Erscheinungstermin: 14. Juli 2021
• Englisch
• ISBN-13: 9781119578185
• Artikelnr.: 62322236

Autorenporträt

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.

Inhaltsangabe

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

1.9 "Training" and "Testing" Models: What "Statistical Learning" Means in the Age of Machine Learning and Data Science 20

1.10 Where We Are Going From Here: How to Use This Book 22

Review Exercises 23

2 Introduction to Python and the Field of Computational Statistics 25

2.1 The Importance of Specializing in Statistics and Research, Not Python: Advice for Prioritizing Your Hierarchy 26

2.2 How to Obtain Python 28

2.3 Python Packages 29

2.4 Installing a New Package in Python 31

2.5 Computing z-Scores in Python 32

2.6 Building a Dataframe in Python: And Computing Some Statistical Functions 35

2.7 Importing a .txt or .csv File 38

2.8 Loading Data into Python 39

2.9 Creating Random Data in Python 40

2.10 Exploring Mathematics in Python 40

2.11 Linear and Matrix Algebra in Python: Mechanics of Statistical Analyses 41

2.11.1 Operations on Matrices 44

2.11.2 Eigenvalues and Eigenvectors 47

Review Exercises 48

3 Visualization in Python: Introduction to Graphs and Plots 50

3.1 Aim for Simplicity and Clarity in Tables and Graphs: Complexity is for Fools! 52

3.2 State Population Change Data 54

3.3 What Do the Numbers Tell Us? Clues to Substantive Theory 56

3.4 The Scatterplot 58

3.5 Correlograms 59

3.6 Histograms and Bar Graphs 61

3.7 Plotting Side-by-Side Histograms 62

3.8 Bubble Plots 63

3.9 Pie Plots 65

3.10 Heatmaps 66

3.11 Line Charts 68

3.12 Closing Thoughts 69

Review Exercises 70

4 Simple Statistical Techniques for Univariate and Bivariate Analyses 72

4.1 Pearson Product-Moment Correlation 73

4.2 A Pearson Correlation Does Not (Necessarily) Imply Zero Relationship 75

4.3 Spearman's Rho 76

4.4 More General Comments on Correlation: Don't Let a Correlation Impress You Too Much! 79

4.5 Computing Correlation in Python 80

4.6 T-Tests for Comparing Means 84

4.7 Paired-Samples t-Test in Python 88

4.8 Binomial Test 90

4.9 The Chi-Squared Distribution and Goodness-of-Fit Test 91

4.10 Contingency Tables 93

Review Exercises 94

5 Power, Effect Size, P-Values, and Estimating Required Sample Size Using Python 96

5.1 What Determines the Size of a P-Value? 96

5.2 How P-Values Are a Function of Sample Size 99

5.3 What is Effect Size? 100

5.4 Understanding Population Variability in the Context of Experimental Design 102

5.5 Where Does Power Fit into All of This? 103

5.6 Can You Have Too Much Power? Can a Sample Be Too Large? 104

5.7 Demonstrating Power Principles in Python: Estimating Power or Sample Size 106

5.8 Demonstrating the Influence of Effect Size 108

5.9 The Influence of Significance Levels on Statistical Power 108

5.10 What About Po