Building Better Models with JMP Pro , livre ebook
190
pages
English
Ebooks
2015
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190
pages
English
Ebooks
2015
Obtenez un accès à la bibliothèque pour le consulter en ligne En savoir plus
Publié par
Date de parution
01 août 2015
EAN13
9781629599564
Langue
English
Poids de l'ouvrage
43 Mo
Building Better Models with JMP® Pro provides an example-based introduction to business analytics, with a proven process that guides you in the application of modeling tools and concepts. It gives you the "what, why, and how" of using JMP® Pro for building and applying analytic models. This book is designed for business analysts, managers, and practitioners who may not have a solid statistical background, but need to be able to readily apply analytic methods to solve business problems.
In addition, this book will greatly benefit faculty members who teach any of the following subjects at the lower to upper graduate level: predictive modeling, data mining, and business analytics. Novice to advanced users in business statistics, business analytics, and predictive modeling will find that it provides a peek inside the black box of algorithms and the methods used.
Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross-validation, model comparison and selection, and data reduction techniques. Full of rich examples, Building Better Models with JMP Pro is an applied book on business analytics and modeling that introduces a simple methodology for managing and executing analytics projects. No prior experience with JMP is needed.
Make more informed decisions from your data using this newest JMP book.
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In addition, this book will greatly benefit faculty members who teach any of the following subjects at the lower to upper graduate level: predictive modeling, data mining, and business analytics. Novice to advanced users in business statistics, business analytics, and predictive modeling will find that it provides a peek inside the black box of algorithms and the methods used.
Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross-validation, model comparison and selection, and data reduction techniques. Full of rich examples, Building Better Models with JMP Pro is an applied book on business analytics and modeling that introduces a simple methodology for managing and executing analytics projects. No prior experience with JMP is needed.
Make more informed decisions from your data using this newest JMP book.
Publié par
Date de parution
01 août 2015
EAN13
9781629599564
Langue
English
Poids de l'ouvrage
43 Mo
Building Better Models ® with JMP Pro Jim Grayson • Sam Gardner • Mia L. Stephens
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The correct bibliographic citation for this manual is as follows: Grayson, Jim, Gardner, ® Sam, and Stephens, Mia. 2015.Building Better Models with JMP Pro. Cary, NC: SAS Institute Inc. ® Building Better Models with JMP Pro Copyright © 2015, SAS Institute Inc., Cary, NC, USA ISBN 978-1-62959-056-1 (Hard copy) ISBN 978-1-62959-956-4 (EPUB) ISBN 978-1-62959-957-1 (MOBI) ISBN 978-1-62959-958-8 (PDF) All rights reserved. Produced in the United States of America. FOr à hàrd cOpy bOOk:No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. FOr à web dOwnlOàd Or e-bOOk:Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. GOvernment License ights; estricted ights:The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. federal law, the minimum restricted rights as set out in FAR 52.227-19 (DEC 2007). If FAR 52.227-19 is applicable, this provision serves as notice under clause (c) thereof and no other notice is required to be affixed to the Software or documentation. The Government’s rights in Software and documentation shall be only those set forth in this Agreement. SAS Institute Inc., SAS Campus Drive, Cary, NC 27513-2414 August 2015 ® SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies.
Contents
Acknowledgments About This Book About These Authors Part 1 Introduction Chapter 1 Introduction Overview Analytics Is Hot! What You Will Learn Analytics and Data Mining How the Book Is Organized Let’s Get Started References Chapter 2 An Overview of the Business Analytics Process Introduction Commonly Used Process Models The Business Analytics Process Define the Problem Prepare for Modeling Modeling Deploy Model Monitor Performance Conclusion References Part 2 Preparing for Modeling Chapter 3 Working with Data Introduction JMP Basics Opening JMP and Getting Started JMP Data Tables Examining and Understanding Your Data Preparing Data for Modeling Summary and Getting Help in JMP Exercises References Part 3 Model Building Chapter 4 Multiple Linear Regression In the News Representative Business Problems Preview of End Result Looking Inside the Black Box: How the Algorithm Works Example 1: Housing Prices Applying the Business Analytics Process Summary
Example 2: Bank Revenues Applying the Business Analytics Process Summary Exercises References Chapter 5 Logistic Regression In the News Representative Business Problems Preview of the End Result Looking Inside the Black Box: How the Algorithm Works Example 1: Lost Sales Opportunities Applying the Business Analytics Process Example 2: Titanic Passengers Applying the Business Analytics Process Summary Key Take-Aways and Additional Considerations Exercises References Chapter 6 Decision Trees In the News Representative Business Problems Preview of the End Result Looking Inside the Black Box: How the Algorithm Works Classification Tree for Status Statistical Details Behind Classification Trees Other General Modeling Considerations Exploratory Modeling versus Predictive Modeling Model Cross-Validation Dealing with Missing Values Decision Tree Modeling with Ordinal Predictors Example 1: Credit Card Marketing The Study Applying the Business Analytics Process Case Summary Example 2: Printing Press Yield The Study Applying the Business Analytics Process Case Summary Summary Exercises References Chapter 7 Neural Networks In the News Representative Business Problems Measuring Success Preview of the End Result Looking Inside the Black Box: How the Algorithm Works Neural Networks with Categorical Responses Example 1: Churn
Applying the Business Analytics Process Modeling The Neural Model and Results Case Summary Example 2: Credit Risk Applying the Business Analytics Process Case Summary Summary and Key Take-Aways Exercises References Part 4 Model Selection and Advanced Methods Chapter 8 Using Cross-Validation Overview Why Cross-Validation? Partitioning Data for Cross-Validation Using a Random Validation Portion Specifying the Validation Roles for Each Row K-fold Cross-Validation Using Cross-Validation for Model Fitting in JMP Pro Example Creating Training, Validation, and Test Subsets Examining the Validation Subsets Using Cross-Validation to Build a Linear Regression Model Choosing the Regression Model Terms with Stepwise Regression Making Predictions Using Cross-Validation to Build a Decision Tree Model Fitting a Neural Network Model Using Cross-Validation Model Comparison Key Take-Aways Exercises References Chapter 9 Advanced Methods Overview Concepts in Advanced Modeling Bagging Boosting Regularization Advanced Partition Methods Bootstrap Forest Boosted Tree Boosted Neural Network Models Generalized Regression Models Maximum Likelihood Regression Ridge Regression Lasso Regression Elastic Net Key Take-Aways Exercises
References Chapter 10 Capstone and New Case Studies Introduction Case Study 1: Cell Classification Stage 1: Define the Problem Stage 2: Prepare for Modeling Stage 3: Modeling Case Study 2: Blue Book for Bulldozers (Kaggle Contest) Getting to Know the Data Data Preparation Modeling Model Comparison Next Steps Case Study 3: Default Credit Card, Presenting Results to Management Developing a Management Report Case Study 4: Carvana (Kaggle Contest) Exercises References Appendix Index
Acknowledgments
Dedication To all the people who have patiently encouraged and supported me through this project, especially my wife and children. --SG To Michael, Muffin, and Bubba – thanks for your patience and support! --MS To my parents, John and Beverly, and my wife, Cindy – my great encouragers. --JG Acknowledgments We wish to express our gratitude to the reviewers who provided many helpful suggestions. This book is much better because of your contributions. Thanks to: Michele Boulanger Michael Crotty Goutam Chakraborty William Duckworth Suneel Grover Duane Hayes Matt Liberatore Robert Nydick Dan Obermiller Sue Walsh
About This Book
Purpose This book is designed for the student wanting to prepare for their professional career who recognizes the need for understanding of both the mechanics and the concepts of predominant analytic modeling tools and also how to apply methods in solving real-world business problems. This book is also designed to meet the needs of 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. Is This Book for You? This book is for you if you want to explore the use of analytics for making better business decisions, and have either been intimidated by books that focus on the technical details or discouraged by books that primarily focus on the high level importance of using data without getting to the “how to” of the methods and analysis. Prerequisites While not required, it would be very helpful if the reader has taken a basic course in statistics. Experience with the book software, JMP Pro, is not required. Scope of This Book This book is not an introductory statistics book. While we provide an introduction to basic data analysis, data visualization and analyzing multivariate data, for the most part, the statistical details and background information are not provided. 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. This book will cover analytic topics including: • Regression • Logistic regression • Classification and regression trees • Neural networks • Model cross-validation It will also provide an introduction to some advanced modeling techniques (boosting, bagging, and regularization) in an example driven structure. About the Examples Software Used to Develop the Book’s Content JMP Pro 12 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 athttp://support.sas.com/publishing/authors. Select the name of the author. Then, look for the cover thumbnail of this book, and select Example Code and Data to display the SAS programs that are included in this book. Some resources, such as instructor resources and add-ins used in the book, will be posted on the JMP community file exchange (community.jmp.com). For an alphabetical listing of all books for which example code and data is available, seehttp://support.sas.com/bookcode. Select a title to display the book’s example code. If you are unable to access the code through the Web site, send e-mail to saspress@sas.com. Exercise Solutions We strongly believe that for the reader to obtain maximum benefit from this book they should “play along” and complete the examples demonstrated in each chapter. Also, at the end of each chapter are suggested exercises to practice what has been discussed in the chapter. Additional Help Although this book illustrates many analyses regularly performed in businesses across industries, questions specific to your aims and issues may arise. To fully support you, SAS Institute and SAS Press offer you the following help resources: • For questions about topics covered in this book, contact the author through SAS Press: ∘questions by email to Send saspress@sas.com; include the book title in your correspondence. ∘ Submit feedback on the author’s page at http://support.sas.com/author_feedback. • For questions about topics in or beyond the scope of this book, post queries to the relevant SAS Support Communities at https://communities.sas.com/welcome. • SAS Institute maintains a comprehensive website with up-to-date information. One page that is particularly useful to both the novice and the seasoned SAS user is its Knowledge Base. Search for relevant notes in the “Samples and SAS Notes” section of the Knowledge Base athttp://support.sas.com/resources. • Registered SAS users or their organizations can access SAS Customer Support athttp://support.sas.com. Here you can pose specific questions to SAS Customer Support; underSupport, clickSubmit a Problem. You will need to provide an email address to which replies can be sent, identify your organization, and provide a customer site number or license information. This information can be found in your SAS logs. Keep in Touch We look forward to hearing from you. We invite questions, comments, and concerns.
