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Statistical Analysis with Python For Dummies

Joseph Schmuller (Author)

9781394370320, Wiley

Paperback / softback, published 18 December 2025

416 pages
23.4 x 18.8 x 2.8 cm, 0.544 kg

Wrangle stats as you learn how to graph, analyze, and interpret data with Python

Statistical Analysis with Python For Dummies introduces you to the tool of choice for digging deep into data to inform business decisions. Even if you're new to coding, this book unlocks the magic of Python and shows you how to apply it to statistical analysis tasks. You'll learn to set up a coding environment and use Python's libraries and functions to mine data for correlations and test hypotheses. You'll also get a crash course in the concepts of probability, including graphing and explaining your results. Part coding book, part stats class, part business analyst guide, this book is ideal for anyone tasked with squeezing insight from data.

  • Get clear explanations of the basics of statistics and data analysis
  • Learn how to summarize and analyze data with Python, step by step
  • Improve business decisions with objective evidence and analysis
  • Explore hypothesis testing, regression analysis, and prediction techniques

This is the perfect introduction to Python for students, professionals, and the stat-curious.

Introduction 1

Part 1: Getting Started with Statistical Analysis with Python 7

Chapter 1: Data, Statistics, and Decisions 9

Chapter 2: Python: What It Does and How It Does It 17

Part 2: Describing Data 45

Chapter 3: Getting Graphic 47

Chapter 4: Finding Your Center 61

Chapter 5: Deviating from the Average 73

Chapter 6: Meeting Standards and Standings 83

Chapter 7: Summarizing It All 93

Chapter 8: What’s Normal? 105

Part 3: Drawing Conclusions from Data 121

Chapter 9: The Confidence Game: Estimation 123

Chapter 10: One-Sample Hypothesis Testing 137

Chapter 11: Two-Sample Hypothesis Testing 159

Chapter 12: Testing More than Two Samples 181

Chapter 13: More Complicated Testing 211

Chapter 14: Regression: Linear, Multiple, and the General Linear Model 233

Chapter 15: Correlation: The Rise and Fall of Relationships 273

Chapter 16: Curvilinear Regression: When Relationships Get Complicated 289

Part 4: Working with Probability 317

Chapter 17: Introducing Probability 319

Chapter 18: Introducing Modeling 341

Chapter 19: Probability Meets Regression: Logistic Regression 363

Part 5: The Part of Tens 373

Chapter 20: Ten Tips for R Veterans 375

Chapter 21: Ten Valuable Python Resources 383

Index 387

Subject Areas: Mathematics [PB]
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