1 option
R by Example / by Jim Albert, Maria Rizzo.
Springer Nature - Springer Mathematics and Statistics eBooks 2024 English International Available online
View online- Format:
- Book
- Author/Creator:
- Albert, Jim.
- Series:
- Use R!, 2197-5744
- Language:
- English
- Subjects (All):
- Statistics.
- Mathematical statistics--Data processing.
- Mathematical statistics.
- Computer science--Mathematics.
- Computer science.
- Coding theory.
- Information theory.
- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
- Statistics and Computing.
- Probability and Statistics in Computer Science.
- Statistical Theory and Methods.
- Coding and Information Theory.
- Local Subjects:
- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
- Statistics and Computing.
- Probability and Statistics in Computer Science.
- Statistical Theory and Methods.
- Coding and Information Theory.
- Physical Description:
- 1 online resource (461 pages)
- Edition:
- 2nd ed. 2024.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
- Summary:
- Now in its second edition, 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, it 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. The new edition includes expanded coverage of ggplot2 graphics, as well as new chapters on importing data and multivariate data methods.
- Contents:
- Introduction
- Quantitative Data
- Categorical Data
- Exploratory Data Analysis
- Presentation Graphics
- Importing Data
- Basic Inference Models
- Regression
- Analysis of Variance I
- Analysis of Variance II
- Randomization Tests
- Multivariate Data
- Simulation Experiments
- Bayesian Modeling
- Monte Carlo Methods.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-76074-3
- OCLC:
- 1478692039
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.