Ebook: R by Example: Concepts to Code
Author: Jim Albert Maria Rizzo (auth.)
- Tags: Statistical Theory and Methods
- Series: Use R!
- Year: 2012
- Publisher: Springer-Verlag New York
- Edition: 1
- Language: English
- pdf
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
Content:
Front Matter....Pages i-xiii
Introduction....Pages 1-42
Quantitative Data....Pages 43-78
Categorical data....Pages 79-99
Presentation Graphics....Pages 101-131
Exploratory Data Analysis....Pages 133-151
Basic Inference Methods....Pages 153-172
Regression....Pages 173-197
Analysis of Variance I....Pages 199-225
Analysis of Variance II....Pages 227-241
Randomization Tests....Pages 243-253
Simulation Experiments....Pages 255-276
Bayesian Modeling....Pages 277-305
Monte Carlo Methods....Pages 307-336
Back Matter....Pages 337-359
R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.
Content:
Front Matter....Pages i-xiii
Introduction....Pages 1-42
Quantitative Data....Pages 43-78
Categorical data....Pages 79-99
Presentation Graphics....Pages 101-131
Exploratory Data Analysis....Pages 133-151
Basic Inference Methods....Pages 153-172
Regression....Pages 173-197
Analysis of Variance I....Pages 199-225
Analysis of Variance II....Pages 227-241
Randomization Tests....Pages 243-253
Simulation Experiments....Pages 255-276
Bayesian Modeling....Pages 277-305
Monte Carlo Methods....Pages 307-336
Back Matter....Pages 337-359
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