B. D. McCullough 
Business Experiments with R 

BUSINESS EXPERIMENTS with R

A unique text that simplifies experimental business design and is dedicated to the R language

Business Experiments with R offers a guide to, and explores the fundamentals of experimental business designs. The book fills a gap in the literature to provide a text on the topic of business statistics that addresses issues such as small samples, lack of normality, and data confounding. The author–a noted expert on the topic–puts the focus on the A/B tests (and their variants) that are widely used in industry, but not typically covered in business statistics textbooks.

The text contains the tools needed to design and analyze two-treatment experiments (i.e., A/B tests) to answer business questions. The author highlights the strategic and technical issues involved in designing experiments that will truly affect organizations. The book then builds on the foundation in Part I and expands the multivariable testing. Since today’s companies are using experiments to solve a broad range of problems, Business Experiments with R is an essential resource for any business student. This important text:

* Presents the key ideas that business students need to know about experiments

* Offers a series of examples, focusing on a specific business question

* Helps develop the ability to frame ill-defined problems and determine what data and analysis would provide information about that problem

Written for students of general business, marketing, and business analytics, Business Experiments with R is an important text that helps to answer business questions by highlighting the strategic and technical issues involved in designing experiments that will truly affect organizations.

Spis treści

Preface xiii

Suggested courses using this book xv

Acknowledgments xix

1 Why Experiment? 1

2 Analyzing A/B Tests: Basics 49

3 Designing A/B Tests with Large Samples 107

4 Analyzing A/B Tests: Advanced Techniques 127

5 Designing Tests with Small Samples 189

6 Analyzing Designs via Regression 229

7 Two-Level Full Factorial Experiments 281

8 Two-Level Screening Designs 329

9 Custom Design of Experiments 357

10 Epilogue 397

Index 419

O autorze

B. D. MCCULLOUGH, PHD, was a Professor in the Department of Decision Sciences & MIS, Le Bow College of Business, Drexel University, Philadelphia, PA.