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Basic Statistics with R. Reaching Decisions with Data

  • Book

  • June 2021
  • Elsevier Science and Technology
  • ID: 5238275

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.

Table of Contents

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

Authors

Stephen C. Loftus Analyst, Research & Development, Atlanta Braves Baseball Club. Dr. Stephen Loftus is an Analyst in Research & Development for the Atlanta Braves. Prior to this, he held academic positions at Randolph-Macon College and Sweet Briar College. In his experience in academia and industry, Dr. Loftus has spent a great deal of time studying and developing Bayesian models for a variety of projects. These highly collaborative projects range from analysis in baseball to studies in numerical ecology. In developing these models, he found himself, on many occasions, needing to explain not only the decisions made in making these models, but also the rationale behind the Bayesian philosophy of statistics to individuals with diverse mathematical backgrounds.