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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

Summary

The original data available to a data scientist/analyst is only the starting point of the discovery process. Building additional fields, both to address issues in the data and to incorporate subject matter expertise by creating derived variables, is central to predictive analytics. SPSS provides a range of tools to facilitate the development of new data fields, using a very comprehensive set of mathematical and statistical functions. Essential tasks, such as revising or grouping codes can be handled using specially designed transformation commands that simplify the process. Transformations that require more complex rules can be performed using conditional logic and the nesting of the appropriate commands.

In this chapter, the most commonly used transformation commands--RECODE, COMPUTE and IF--were explored in detail. The more complex DO IF/END IF structure was introduced...

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