Simple Predictive Analytics: Using Excel to Solve Business Problems Contributor(s): Seare, Curtis (Author) |
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ISBN: 1795224738 ISBN-13: 9781795224734 Publisher: Independently Published OUR PRICE: $5.69 Product Type: Paperback Published: January 2019 |
Additional Information |
BISAC Categories: - Computers | Data Modeling & Design |
Physical Information: 0.19" H x 8.5" W x 11.02" (0.51 lbs) 90 pages |
Descriptions, Reviews, Etc. |
Publisher Description: This book will give you the critical information you need to create, use, and validate simple predictive models, and it will suggest the types of real-world business problems you can solve with those models. It is designed to be as simple as possible, providing basic, practical, and immediately applicable information for business users new to the world of predictive modeling. In summary:
Our approach is as follows: First, introduce analysis fundamentals. These are the basics of doing good and accurate analysis, and it will be important to keep these principles in mind as you create predictive models. Second, explain the process that will allow you to follow some easy, predefined steps to creating your own predictive models. This is a "big-picture" process flow meant to give you a basic procedure to follow no matter what type of predictive model you need to create. Last, this guide gives you an in-depth look into various predictive modeling techniques, organized according to the type of data you have and the type of questions you're trying to answer. This section makes up the bulk of the book, and the explanation of each model tells you what the predictive model looks like, what it can be used for, the assumptions necessary to use the model, a process to follow to create it (including step-by-step instructions in Excel), an explanation of some common errors to watch for, and a section on analyzing your results. The modeling process you will learn is as follows: 1. Choose a predictive model according to the business question. 2. Check to see if all the conditions for the model are met. 3. Carry out the analysis. 4. Check for statistical significance and fit. 5. Validate the predictive model. 6. Refine the predictive model. The basic models we go over in this text:
These models cover four common prediction cases you will encounter:
What you will not get in this book:
What you will get in this book:
Also, this book may or may not mention wombats. |