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Statistics for Data Science and Analytics Edition: 1
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Statistics for Data Science and Analytics Edition: 1

by Peter C. Bruce; Peter Gedeck; Janet Dobbins

Data Science

Book Details

Book Title

Statistics for Data Science and Analytics Edition: 1

Author

Peter C. Bruce; Peter Gedeck; Janet Dobbins

Publisher

Wiley

Publication Date

2024

ISBN

9781394253807

Number of Pages

366

Language

English

Format

PDF

File Size

3.8MB

Subject

Computers > Software: Systems: scientific computing

Table of Contents

  • Front Matter
  • Title Page
  • Copyright
  • Contents
  • About the Authors
  • Acknowledgments
  • About the Companion Website
  • Introduction
  • Chapter 1
  • 1.1 Big Data: Predicting Pregnancy
  • 1.2 Phantom Protection from Vitamin E
  • 1.3 Statistician, Heal Thyself
  • 1.4 Identifying Terrorists in Airports
  • 1.5 Looking Ahead
  • 1.6 Big Data and Statisticians
  • Chapter 2
  • 2.1 Statistical Science
  • 2.2 Big Data
  • 2.3 Data Science
  • 2.4 Example: Hospital Errors
  • 2.5 Experiment
  • 2.6 Designing an Experiment
  • 2.7 The Data
  • 2.8 Variables and Their Flavors
  • 2.9 Python: Data Structures and Operations
  • 2.10 Are We Sure We Made a Difference?
  • 2.11 Is Chance Responsible? The Foundation of Hypothesis Testing
  • 2.12 Probability
  • 2.13 Significance or Alpha Level
  • 2.14 Other Kinds of Studies
  • 2.15 When to Use Hypothesis Tests
  • 2.16 Experiments Falling Short of the Gold Standard
  • 2.17 Summary
  • 2.18 Python: Iterations and Conditional Execution
  • 2.19 Python: Numpy, scipy, and pandas—The Workhorses of Data Science
  • Exercises
  • Chapter 3
  • 3.1 Exploratory Data Analysis
  • 3.2 What to Measure—Central Location
  • 3.3 What to Measure—Variability
  • 3.4 What to Measure—Distance (Nearness)
  • 3.5 Test Statistic
  • 3.6 Examining and Displaying the Data
  • 3.7 Python: Exploratory Data Analysis/Data Visualization
  • Exercises
  • Chapter 4
  • 4.1 Avoid Being Fooled by Chance
  • 4.2 The Null Hypothesis
  • 4.3 Repeating the Experiment
  • 4.4 Statistical Significance
  • 4.5 Power
  • 4.6 The Normal Distribution
  • 4.7 Summary
  • 4.8 Python: Random Numbers
  • Exercises
  • Chapter 5
  • 5.1 What Is Probability
  • 5.2 Simple Probability
  • 5.3 Probability Distributions
  • 5.4 From Binomial to Normal Distribution
  • 5.5 Appendix: Binomial Formula and Normal Approximation
  • 5.6 Python: Probability
  • Exercises
  • Chapter 6
  • 6.1 Two‐way Tables
  • 6.2 Conditional Probability
  • 6.3 Bayesian Estimates
  • 6.4 Independence
  • 6.5 Multiplication Rule
  • 6.6 Simpson's Paradox
  • 6.7 Python: Counting and Contingency Tables
  • Exercises
  • Chapter 7
  • 7.1 Literary Digest—Sampling Trumps “All Data”
  • 7.2 Simple Random Samples
  • 7.3 Margin of Error: Sampling Distribution for a Proportion
  • 7.4 Sampling Distribution for a Mean
  • 7.5 The Bootstrap
  • 7.6 Rationale for the Bootstrap
  • 7.7 Standard Error
  • 7.8 Other Sampling Methods
  • 7.9 Absolute vs. Relative Sample Size
  • 7.10 Python: Random Sampling Strategies
  • Exercises
  • Chapter 8
  • 8.1 Count Data—R × C Tables
  • 8.2 The Role of Experiments (Many Are Costly)
  • 8.3 Chi‐Square Test
  • 8.4 Single Sample—Goodness‐of‐Fit
  • 8.5 Numeric Data: ANOVA
  • 8.6 Components of Variance
  • 8.7 Factorial Design
  • 8.8 The Problem of Multiple Inference
  • 8.9 Continuous Testing
  • 8.10 Bandit Algorithms
  • 8.11 Appendix: ANOVA, the Factor Diagram, and the F‐Statistic
  • 8.12 More than One Factor or Variable—From ANOVA to Statistical Models
  • 8.13 Python: Contingency Tables and Chi‐square Test
  • 8.14 Python: ANOVA
  • Exercises
  • Chapter 9
  • 9.1 Example: Delta Wire
  • 9.2 Example: Cotton Dust and Lung Disease
  • 9.3 The Vector Product Sum Test
  • 9.4 Correlation Coefficient
  • 9.5 Correlation is not Causation
  • 9.6 Other Forms of Association
  • 9.7 Python: Correlation
  • Exercises
  • Chapter 10
  • 10.1 Finding the Regression Line by Eye
  • 10.2 Finding the Regression Line by Minimizing Residuals
  • 10.3 Linear Relationships
  • 10.4 Prediction vs. Explanation
  • 10.5 Python: Linear Regression
  • Exercises
  • Chapter 11
  • 11.1 Terminology
  • 11.2 Example—Housing Prices
  • 11.3 Interaction
  • 11.4 Regression Assumptions
  • 11.5 Assessing Explanatory Regression Models
  • 11.6 Assessing Regression for Prediction
  • 11.7 Python: Multiple Linear Regression
  • Exercises
  • Chapter 12
  • 12.1 K‐Nearest‐Neighbors
  • 12.2 Python: Classification
  • Exercises
  • Index