Book contents
- Introduction to Probability and Statistics for Data Science
- Reviews
- Introduction to Probability and Statistics for Data Science
- Copyright page
- Dedication
- Contents
- Preface
- 1 Introduction
- 2 Data Visualization
- 3 Basic Probability
- 4 Random Variables
- 5 Discrete Distributions
- 6 Continuous Distributions
- 7 About Data and Data Collection
- 8 Sampling Distributions
- 9 Point Estimation
- 10 Confidence Intervals
- 11 Hypothesis Testing
- 12 Hypothesis Tests for Two or More Populations
- 13 Hypothesis Tests for Categorical Data
- 14 Regression
- 15 Bayesian Methods
- 16 Time Series Methods
- 17 Estimating the Standard Error: Analytic Approximations, the Jackknife, and the Bootstrap
- 18 Generalized Linear Models and Regression Trees
- 19 Cross-Validation and Estimates of Prediction Error
- 20 Large-Scale Hypothesis Testing
- References
- Index
- References
References
Published online by Cambridge University Press: 13 December 2024
- Introduction to Probability and Statistics for Data Science
- Reviews
- Introduction to Probability and Statistics for Data Science
- Copyright page
- Dedication
- Contents
- Preface
- 1 Introduction
- 2 Data Visualization
- 3 Basic Probability
- 4 Random Variables
- 5 Discrete Distributions
- 6 Continuous Distributions
- 7 About Data and Data Collection
- 8 Sampling Distributions
- 9 Point Estimation
- 10 Confidence Intervals
- 11 Hypothesis Testing
- 12 Hypothesis Tests for Two or More Populations
- 13 Hypothesis Tests for Categorical Data
- 14 Regression
- 15 Bayesian Methods
- 16 Time Series Methods
- 17 Estimating the Standard Error: Analytic Approximations, the Jackknife, and the Bootstrap
- 18 Generalized Linear Models and Regression Trees
- 19 Cross-Validation and Estimates of Prediction Error
- 20 Large-Scale Hypothesis Testing
- References
- Index
- References
- Type
- Chapter
- Information
- Introduction to Probability and Statistics for Data Sciencewith R, pp. 805 - 808Publisher: Cambridge University PressPrint publication year: 2024