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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

Outliers

An outlier is an observation that lies an unusual distance from other observations. There is a judgmental element in deciding what is considered unusual, and it helps to work with the subject-matter expert in deciding this. In exploratory data analysis, there are two activities that are linked:

  • Examining the overall shape of the graphed data for important features
  • Examining the data for unusual observations that are far from the mass or general trend of the data

Outliers are data points that deserve a closer look. The values could be real data values accurately recorded or the values could be misrecorded or otherwise flawed data. You need to discern what is the case in your situation and decide what action to take.

In this section, we consider statistical and graphical ways of summarizing the distribution of a variable and detecting unusual/extreme values. IBM SPSS...

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