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Data Analysis with IBM SPSS Statistics

You're reading from  Data Analysis with IBM SPSS Statistics

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787283817
Pages 446 pages
Edition 1st Edition
Languages
Authors (2):
Ken Stehlik-Barry Ken Stehlik-Barry
Profile icon Ken Stehlik-Barry
Anthony Babinec Anthony Babinec
Profile icon Anthony Babinec
View More author details

Table of Contents (17) Chapters

Preface 1. Installing and Configuring SPSS 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

Example data

The data analyzed in this chapter is the Wine dataset found in the UC-Irvine Machine Learning repository. The data is the result of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 chemical components found in each of the three types of wine. There are 59, 71, and 48 instances respectively in the three classes. The class codes are 1, 2, and 3.

The attributes are as follows:

  • Alcohol
  • Malic acid
  • Ash
  • Alcalinity of ash
  • Magnesium
  • Total phenols
  • Flavanoids
  • Nonflavanoid phenols
  • Proanthocyanins
  • Color intensity
  • Hue
  • OD280/OD315 of diluted wines
  • Proline

In the context of classification, the task is to use the 13 attributes to classify each observation into one of the three wine types. Note that all 13 attributes are numeric.

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