Basic Statistics with R: Reaching Decisions with Data provides an understanding of the processes at work in using data for results. Sections cover data collection and discuss exploratory analyses, including visual graphs, numerical summaries, and relationships between variables - basic probability, and statistical inference - including hypothesis testing and confidence intervals. All topics are taught using real-data drawn from various fields, including economics, biology, political science and sports. Using this wide variety of motivating examples allows students to directly connect and make statistics essential to their field of interest, rather than seeing it as a separate and ancillary knowledge area.
In addition to introducing students to statistical topics using real data, the book provides a gentle introduction to coding, having the students use the statistical language and software R. Students learn to load data, calculate summary statistics, create graphs and do statistical inference using R with either Windows or Macintosh machines.
- Features real-data to give students an engaging practice to connect with their areas of interest
- Evolves from basic problems that can be worked by hand to the elementary use of opensource R software
- Offers a direct, clear approach highlighted by useful visuals and examples
Dr. Stephen C. Loftus is a Visiting Assistant Professor of Mathematics at Sweet Briar College. Prior to this, he received a Ph.D. in Statistics from Virginia Tech where his dissertation discussed Bayesian model selection techniques in high-dimensional omics datasets. Additionally, he worked as an Analyst in Baseball Research and Development for the Tampa Bay Rays baseball club, where his work focused on player evaluation. His research has been presented at the Joint Statistical Meetings and Society of American Baseball Research's Analytics Conference, and he has served as a Judge for the American Statistical Association's Statsketball contest since its inception in 2017. Dr. Loftus' research interests include Bayesian modeling techniques, focusing on applications in text mining, clustering techniques, and sabermetrics
1. Statistics: What is it and Why is it Important? 2. An Introduction to R 3. Data Collection: Methods and Concerns 4. R Tutorial: Subsetting Data 5. Exploratory Data Analyses (EDA) 6. Libraries, Loading Data, and EDA in R 7. An Incredibly Brief Introduction to Probability 8. Sampling Distributions, or Why EDA is not Enough 9. The Idea of Hypothesis Testing 10. Hypothesis Testing with the Central Limit Theorem 11. Introduction to Confidence Intervals 12. One Sample Hypothesis Tests 13. Confidence Intervals for a Single Parameter 14. Two Sample Hypothesis Tests 15. Confidence Intervals for Two Parameters 16. Hypothesis Testing and Confidence Intervals in R 17. Statistics: The World Beyond This Book