Statistical Analysis with Python For Dummies

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