| Description |
1 online resource |
| Contents |
Introduction -- An overview of the business analytics process -- Working with data -- Multiple linear regression -- Logistic regression -- Decision trees -- Neural networks -- Using cross-validation -- Advanced methods -- Capstone and new case studies |
| Summary |
This book 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. It 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. -- Edited summary from book |
| Bibliography |
Includes bibliographical references and index |
| Notes |
English |
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Vendor-supplied metadata |
| SUBJECT |
JMP (Computer file) http://id.loc.gov/authorities/names/n97119732
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JMP (Computer file) fast |
| Subject |
Mathematical statistics -- Data processing.
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Statistics -- Graphic methods.
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MATHEMATICS -- Applied.
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MATHEMATICS -- Probability & Statistics -- General.
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COMPUTERS -- Mathematical & Statistical Software.
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Mathematical statistics -- Data processing
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Statistics -- Graphic methods
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Mathematical Statistics.
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Mathematics.
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Physical Sciences & Mathematics.
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| Form |
Electronic book
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| Author |
Gardner, Sam
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|
Stephens, Mia L.
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| LC no. |
2016427726 |
| ISBN |
9781629599588 |
|
1629599581 |
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9781629599564 |
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1629599565 |
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