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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 FREE CHAPTER 2. Accessing and Organizing Data 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

Descriptive discriminant analysis

One purpose of discriminant analysis is description--finding a way to separate and characterize the three species in terms of differences on the classifying variables. In the Iris data, Fisher saw that size matters--members of a certain species tend to have larger values for dimensional measurements on the individual samples such as petal length and width and sepal length and width. In addition, there was another pattern--members of a certain species that otherwise had small dimensional measurements on three of the indicators had relatively large sepal widths. Taking into account both of these patterns, one is able to classify irises with great accuracy as well as understand what characterizes exemplars of each species.

In descriptive discriminant analysis, you would report and focus on summary statistics within groups such as means, standard...

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