Buch, Englisch, 416 Seiten, Format (B × H): 188 mm x 232 mm, Gewicht: 567 g
Buch, Englisch, 416 Seiten, Format (B × H): 188 mm x 232 mm, Gewicht: 567 g
ISBN: 978-1-394-37032-0
Verlag: Wiley
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.
Autoren/Hrsg.
Weitere Infos & Material
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
