About This Book
What Does This Book Cover?
This book focuses on the business statistics intelligence component of business analytics. It covers processes to perform a statistical study that may include data mining or predictive analytics techniques. Some real-world business examples of using these techniques are as follows:
● target marketing
● customer relation management
● market basket analysis
● cross-selling
● market segmentation
● customer retention
● improved underwriting
● quality control
● competitive analysis
● fraud detection and management
● churn analysis
Specific applications can be found at http://www.jmp.com/software/success. The bottom line, as reported by the KDNuggets poll (2008), is this: The median return on investment for data mining projects is in the 125–150% range. (See http://www.kdnuggets.com/polls/2008/roi-data-mining.htm.)
This book is not an introductory statistics book, although it does introduce basic data analysis, data visualization, and analysis of multivariate data. For the most part, your introductory statistics course has not completely prepared you to move on to real-world statistical analysis. The primary objective of this book is, therefore, to provide a bridge from your introductory statistics course to practical statistical analysis. This book is also not a highly technical book that dives deeply into the theory or algorithms, but it will provide insight into the “black box” of the methods covered. Analytics techniques covered by this book include the following:
● regression
● ANOVA
● logistic regression
● principal component analysis
● LASSO and Elastic Net
● cluster analysis
● decision trees
● k-nearest neighbors
● neural networks
● bootstrap forests and boosted trees
● text mining
● association rules
Is This Book for You?
This book is designed for the student who wants to prepare for his or her professional career and who recognizes the need to understand both the concepts and the mechanics of predominant analytic modeling tools for solving real-world business problems. This book is designed also for the practitioner who wants to obtain a hands-on understanding of business analytics to make better decisions from data and models, and to apply these concepts and tools to business analytics projects.
This book is for you if you want to explore the use of analytics for making better business decisions and have been either intimidated by books that focus on the technical details, or discouraged by books that focus on the high-level importance of using data without including the how-to of the methods and analysis.
Although not required, your completion of a basic course in statistics will prove helpful. Experience with the book’s software, JMP Pro 13, is not required.
What’s New in This Edition?
This second edition includes six new chapters. The topics of these new chapters are dirty data, LASSO and elastic net, k-nearest neighbors, bootstrap forests and boosted trees, text mining, and association rules. All the old chapters from the first edition are updated to JMP 13. In addition, more end-of-chapter exercises are provided.
What Should You Know about the Examples?
This book includes tutorials for you to follow to gain hands-on experience with SAS.
Software Used to Develop the Book's Content
JMP Pro 13 is the software used throughout this book.
Example Code and Data
You can access the example code and data for this book by linking to its author page at http://support.sas.com/authors. Some resources, such as instructor resources and add-ins used in the book, can be found on the JMP User Community file exchange at https://community.jmp.com.
Where Are the Exercise Solutions?
We strongly believe that for you to obtain maximum benefit from this book you need to complete the examples in each chapter. At the end of most chapters are suggested exercises so that you can practice what has been discussed in the chapter. Exercises for additional chapters, exercises in the book, and exercise solutions are available as a complete set. Professors and instructors can obtain them by requesting them through the authors’ SAS Press webpages at http://support.sas.com/authors.
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