Statistics for Data Science and Analytics

Table of Contents

1. Cover
2. Table of Contents
3. Title Page
4. Copyright
5. Dedication
6. About the Authors
7. Acknowledgments
8. About the Companion Website
9. Introduction
   1. Statistics and Data Science
   2. Accompanying Web Resources
   3. Python
   4. Using Python with this Book
10. 1 Statistics and Data Science
    1. 1.1 Big Data: Predicting Pregnancy
    2. 1.2 Phantom Protection from Vitamin E
    3. 1.3 Statistician, Heal Thyself
    4. 1.4 Identifying Terrorists in Airports
    5. 1.5 Looking Ahead
    6. 1.6 Big Data and Statisticians
11. 2 Designing and Carrying Out a Statistical Study
    1. 2.1 Statistical Science
    2. 2.2 Big Data
    3. 2.3 Data Science
    4. 2.4 Example: Hospital Errors
    5. 2.5 Experiment
    6. 2.6 Designing an Experiment
    7. 2.7 The Data
    8. 2.8 Variables and Their Flavors
    9. 2.9 Python: Data Structures and Operations
    10. 2.10 Are We Sure We Made a Difference?
    11. 2.11 Is Chance Responsible? The Foundation of Hypothesis Testing
    12. 2.12 Probability
    13. 2.13 Significance or Alpha Level
    14. 2.14 Other Kinds of Studies
    15. 2.15 When to Use Hypothesis Tests
    16. 2.16 Experiments Falling Short of the Gold Standard
    17. 2.17 Summary
    18. 2.18 Python: Iterations and Conditional Execution
    19. 2.19 Python: Numpy, scipy, and pandas—The Workhorses of Data Science
    20. Exercises
    21. Notes
12. 3 Exploring and Displaying the Data
    1. 3.1 Exploratory Data Analysis
    2. 3.2 What to Measure—Central Location
    3. 3.3 What to Measure—Variability
    4. 3.4 What to Measure—Distance (Nearness)
    5. 3.5 Test Statistic
    6. 3.6 Examining and Displaying the Data
    7. 3.7 Python: Exploratory ...