## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

No credit card required ## Book Description

"

R is a popular and growing open source statistical analysis and graphics environment as well as a programming language and platform. If you need to use a variety of statistics, then Using R for Statistics will get you the answers to most of the problems you are likely to encounter.

Using R for Statistics is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background.

After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics.

"

1. Cover
2. Title
4. Contents at a Glance
5. Contents
6. About the Author
7. About the Technical Reviewer
8. Acknowledgments
9. Introduction
10. Chapter 1: R Fundamentals
2. Getting Orientated
3. The R Console and Command Prompt
4. Functions
5. Objects
6. The Data Editor
7. Workspaces
8. Error Messages
9. Script Files
10. Summary
11. Chapter 2: Working with Data Files
1. Entering Data Directly
2. Importing Plain Text Files
3. Importing Excel Files
4. Importing Files from Other Software
5. Using Relative File Paths
6. Exporting Datasets
7. Summary
12. Chapter 3: Preparing and Manipulating Your Data
1. Variables
2. Calculating New Numeric Variables
3. Dividing a Continuous Variable into Categories
4. Working with Factor Variables
5. Manipulating Character Variables
6. Working with Dates and Times
7. Adding and Removing Observations
8. Selecting a Subset of the Data
9. Sorting a Dataset
10. Summary
13. Chapter 4: Combining and Restructuring Datasets
1. Appending Rows
2. Appending Columns
3. Merging Datasets by Common Variables
4. Stacking and Unstacking a Dataset
5. Reshaping a Dataset
6. Summary
14. Chapter 5: Summary Statistics for Continuous Variables
1. Univariate Statistics
2. Statistics by Group
3. Measures of Association
4. Hypothesis Test of Correlation
5. Comparing a Sample with a Specified Distribution
6. Confidence Intervals and Prediction Intervals
7. Summary
15. Chapter 6: Tabular Data
1. Frequency Tables
2. Chi-Square Goodness-of-Fit Test
3. Tests of Association Between Categorical Variables
4. Proportions test
5. Summary
16. Chapter 7: Probability Distributions
17. Chapter 8: Creating Plots
18. Chapter 9: Customizing Your Plots
1. Titles and Labels
2. Axes
3. Colors
4. Plotting Symbols
5. Plotting Lines
7. Adding Items to Plots
8. Overlaying Plots
9. Adding a Legend
10. Multiple Plots in the Plotting Area
11. Changing the Default Plot Settings
12. Summary
19. Chapter 10: Hypothesis Testing
1. Student’s T-Tests
2. Wilcoxon Rank-Sum Test
3. Analysis of Variance
4. Kruskal-Wallis Test
5. Multiple Comparison Methods
6. Hypothesis Tests for Variance
7. Summary
20. Chapter 11: Regression and General Linear Models
1. Building the Model
2. Assessing the Fit of the Model
3. Coefficient Estimates
4. Plotting the Line of Best Fit
5. Model Diagnostics
6. Making Predictions
7. Summary
21. Appendix A: Add-On Packages
1. Viewing a List of Available Add-on Packages