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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

Adding and Matching Files

You often need to combine data from multiple sources. For example, you might have customer information such as personal characteristics and purchase history in a customer database. Then, you learn that the marketing department has conducted an attitudinal survey on a subset of your customers, giving rise to new measures on some of your customers. Combining these two data sources enables you to analyze all of the variables together, which can lead to new insights and better predictions of customer behavior.

The preceding scenario describing customer data and survey data is an example of relational data, which means that there are relationship between pairs of datasets. In these datasets, there exist one or more variables called keys that are used to connect each pair of datasets. A key is a variable (or variables) that uniquely identifies an observation...

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