Project-Based R Companion to Introductory Statistics

Description

Project-Based R Companion to Introductory Statistics is envisioned as a companion to a traditional statistics or biostatistics textbook, with each chapter covering traditional topics such as descriptive statistics, regression, and hypothesis testing. However, unlike a traditional textbook, each chapter will present its material using a complete step-by-step analysis of a real publicly... Read more

Project-Based R Companion to Introductory Statistics is envisioned as a companion to a traditional statistics or biostatistics textbook, with each chapter covering traditional topics such as descriptive statistics, regression, and hypothesis testing. However, unlike a traditional textbook, each chapter will present its material using a complete step-by-step analysis of a real publicly available dataset, with an emphasis on the practical skills of testing assumptions, data exploration, and forming conclusions. The chapters in the main body of the book include a worked example showing the R code used at each step followed by a multi-part project for students to complete. These projects, which could serve as alternatives to traditional discrete homework problems, will illustrate how to "put the pieces together" and conduct a complete start-to-finish data analysis using the R statistical software package. At the end of the book, there are several projects that require the use of multiple statistical techniques that could be used as a take-home final exam or final project for a class.

Key features of the text:

Organized in chapters focusing on the same topics found in typical introductory statistics textbooks (descriptive statistics, regression, two-way tables, hypothesis testing for means and proportions, etc.) so instructors can easily pair this supplementary material with course plans

Includes student projects for each chapter which can be assigned as laboratory exercises or homework assignments to supplement traditional homework

Features real-world datasets from scientific publications in the fields of history, pop culture, business, medicine, and forensics for students to analyze

Allows students to gain experience working through a variety of statistical analyses from start to finish

The book is written at the undergraduate level to be used in an introductory statistical methods course or subject-specific research methods course such as biostatistics or research methods for psychology or business analytics.

Author

After a 10-year career as a research biostatistician in the Department of Ophthalmology and Visual Sciences at the University of Wisconsin-Madison, Chelsea Myers teaches statistics and biostatistics at Rollins College and Valencia College in Central Florida. She has authored or co-authored more than 30 scientific papers and presentations and is the creator of the MCAT preparation website MCATMath.com.

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1. Getting Started with R and RStudio
2. Describing Categorical Data
3. Describing Quantitative Data
4. The Normal Distribution
5. Two-Way Tables
6. Linear Regression and Correlation
7. Random Sampling
8. Inference About a Population Mean
9. Inference About a Population Proportion
10. Comparing Two Population Means
11. Comparing Two Population Proportions
12. Student Project 1
13. Student Project 2
14. Student Project 3

Author(s)

Biography

After a 10-year career as a research biostatistician in the Department of Ophthalmology and Visual Sciences at the University of Wisconsin-Madison, Chelsea Myers teaches statistics and biostatistics at Rollins College and Valencia College in Central Florida. She has authored or co-authored more than 30 scientific papers and presentations and is the creator of the MCAT preparation website MCATMath.com. She lives in Winter Park, Florida with her family.

Project-Based R Companion to Introductory Statistics

Description

Table of Contents

Author(s)

Biography

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