Book descriptionDive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization
SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code.
IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results.
- Conduct a more efficient and accurate analysis
- Display complex relationships and create better visualizations
- Model complex interactions and master predictive analytics
- Integrate R and Python with SPSS Statistics for more efficient, more powerful code
These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.
Table of contents
Part I: Advanced Statistics
- Chapter 1: Comparing and Contrasting IBM SPSS AMOS with Other Multivariate Techniques
- Chapter 2: Monte Carlo Simulation and IBM SPSS Bootstrapping
- Chapter 3: Regression with Categorical Outcome Variables
- Chapter 4: Building Hierarchical Linear Models
Part II: Data Visualization
- Chapter 5: Take Your Data Visualizations to the Next Level
- Chapter 6: The Code Behind SPSS Graphics: Graphics Production Language
- Chapter 7: Mapping in IBM SPSS Statistics
- Chapter 8: Geospatial Analytics
- Chapter 9: Perceptual Mapping with Correspondence Analysis, GPL, and OMS
- Chapter 10: Display Complex Relationships with Multidimensional Scaling
Part III: Predictive Analytics
Chapter 11: SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics?
- What Is Data Mining?
- What Is IBM SPSS Modeler?
- Can Data Mining Be Done in SPSS Statistics?
- Hypothesis Testing, Type I Error, and Hold-Out Validation
- Significance of the Model and Importance of Each Independent Variable
- The Importance of Finding and Modeling Interactions
- Classic and Important Data Mining Tasks
- Chapter 12: IBM SPSS Data Preparation
- Chapter 13: Model Complex Interactions with IBM SPSS Neural Networks
- Chapter 14: Powerful and Intuitive: IBM SPSS Decision Trees
- Chapter 15: Find Patterns and Make Predictions with K Nearest Neighbors
- Chapter 11: SPSS Statistics versus SPSS Modeler: Can I Be a Data Miner Using SPSS Statistics?
Part IV: Syntax, Data Management, and Programmability
- Chapter 16: Write More Efficient and Elegant Code with SPSS Syntax Techniques
- Chapter 17: Automate Your Analyses with SPSS Syntax and the Output Management System
- Chapter 18: Statistical Extension Commands
- Title: SPSS Statistics for Data Analysis and Visualization
- Release date: May 2017
- Publisher(s): Wiley
- ISBN: 9781119003557
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