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Data Analysis with IBM SPSS Statistics

You're reading from   Data Analysis with IBM SPSS Statistics Implementing data modeling, descriptive statistics and ANOVA

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787283817
Length 446 pages
Edition 1st Edition
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Authors (2):
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Ken Stehlik-Barry Ken Stehlik-Barry
Author Profile Icon Ken Stehlik-Barry
Ken Stehlik-Barry
Anthony Babinec Anthony Babinec
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Anthony Babinec
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Table of Contents (17) Chapters Close

Preface 1. Installing and Configuring SPSS 2. Accessing and Organizing Data FREE CHAPTER 3. Statistics for Individual Data Elements 4. Dealing with Missing Data and Outliers 5. Visually Exploring the Data 6. Sampling, Subsetting, and Weighting 7. Creating New Data Elements 8. Adding and Matching Files 9. Aggregating and Restructuring Data 10. Crosstabulation Patterns for Categorical Data 11. Comparing Means and ANOVA 12. Correlations 13. Linear Regression 14. Principal Components and Factor Analysis 15. Clustering 16. Discriminant Analysis

Predictive discriminant analysis

A second purpose of discriminant analysis is prediction--developing equations such that if you plug in the input values for a new observed individual or object, the equations would classify the individual or object into one of the target classes.

In modern predictive analytics, discriminant analysis is one of a large number of techniques that could be used in classification. The reason that so many classification techniques exist is that no method dominates the others across all problems and data. Typically, in a project, you might try a number of approaches and compare and contrast their performance on the data. A statistical method such as discriminant analysis could be one of these methods. In the event that the data meet the assumptions of discriminant analysis, it should perform well. As discriminant analysis is an equation-based method, the...

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