Data Analysis with IBM SPSS Statistics: Implementing Data Modeling, Descriptive Statistics and ANOVA

Master data management & analysis techniques with IBM SPSS Statistics 24

About This Book

• Leverage the power of IBM SPSS Statistics to perform efficient statistical analysis of your data
• Choose the right statistical technique to analyze different types of data and build efficient models from your data with ease
• Overcome any hurdle that you might come across while learning the different SPSS Statistics concepts with clear instructions, tips and tricks

Who This Book Is For

This book is designed for analysts and researchers who need to work with data to discover meaningful patterns but do not have the time (or inclination) to become programmers. We assume a foundational understanding of statistics such as one would learn in a basic course or two on statistical techniques and methods.

What You Will Learn

• Install and set up SPSS to create a working environment for analytics
• Techniques for exploring data visually and statistically, assessing data quality and addressing issues related to missing data
• How to import different kinds of data and work with it
• Organize data for analytical purposes (create new data elements, sampling, weighting, subsetting, and restructure your data)
• Discover basic relationships among data elements (bivariate data patterns, differences in means, correlations)
• Explore multivariate relationships
• Leverage the offerings to draw accurate insights from your research, and benefit your decision-making

In Detail

SPSS Statistics is a software package used for logical batched and non-batched statistical analysis. Analytical tools such as SPSS can readily provide even a novice user with an overwhelming amount of information and a broad range of options for analyzing patterns in the data.

The journey starts with installing and configuring SPSS Statistics for first use and exploring the data to understand its potential (as well as its limitations). Use the right statistical analysis technique such as regression, classification and more, and analyze your data in the best possible manner. Work with graphs and charts to visualize your findings. With this information in hand, the discovery of patterns within the data can be undertaken. Finally, the high level objective of developing predictive models that can be applied to other situations will be addressed.

By the end of this book, you will have a firm understanding of the various statistical analysis techniques offered by SPSS Statistics, and be able to master its use for data analysis with ease.

Style and approach

Provides a practical orientation to understanding a set of data and examining the key relationships among the data elements. Shows useful visualizations to enhance understanding and interpretation. Outlines a roadmap that focuses the process so decision regarding how to proceed can be made easily.

Table of Contents

Chapter 1. Installing And Configuring Spss
Chapter 2. Accessing And Organizing Data
Chapter 3. Statistics For Individual Data Elements
Chapter 4. Dealing With Missing Data And Outliers
Chapter 5. Visually Exploring The Data
Chapter 6. Sampling, Subsetting, And Weighting
Chapter 7. Creating New Data Elements
Chapter 8. Adding And Matching Files
Chapter 9. Aggregating And Restructuring Data
Chapter 10. Crosstabulation Patterns For Categorical Data
Chapter 11. Comparing Means And Anova
Chapter 12. Correlations
Chapter 13. Linear Regression
Chapter 14. Principal Components And Factor Analysis
Chapter 15. Clustering
Chapter 16. Discriminant Analysis