Elementary Statistics with Python: Bridge the Gap from SPSS

About the Book

This is the essential guide for anyone looking to modernize their statistical toolkit and seamlessly migrate from traditional software like SPSS to the powerful, open-source environment of Python. Elementary Statistics with Python provides a clear, hands-on roadmap, demystifying fundamental statistical concepts and teaching you how to apply them effectively in a Jupyter-based development environment.

Beyond the code, this book offers a disciplined framework for understanding qualitative versus quantitative data, running performance analytics, and conducting a complete quantitative research study. All research and references within the text adhere to APA citation standards, ensuring academic and professional integrity. It’s the perfect resource for building the confidence and skills needed to transition to a more flexible and powerful data analysis platform.

Target Audience & Prerequisites

This book is specifically designed for:

Social and Soft Science Professionals & Researchers: Individuals who are familiar with traditional statistical methods and are ready to transition to Python.
Students: Those learning introductory statistics who want a practical guide for applying concepts with a modern programming language.
Beginner Data Analysts: Professionals seeking to build a solid foundation in statistical concepts and their application in Python.

A basic understanding of statistical principles is helpful, but no prior Python experience is required.

Full Table of Contents

Chapter 1: Qualitative or Quantitative Data
- The Contextual Corporate Benefit
- Quantitative or Qualitative Survival in Business
Chapter 2: Different Statistical Tools
- Defining Numerical Data
- Importance for SPSS
- The Quantitative Definition of Variable (Nominal, Ordinal, Scale Data)
Chapter 3: Performance Analytics
- Analytical Variable Definitions
- Nomenclature and Ethics
- Analytical Modeling (Independent and Dependent Variables, ANOVA, Understanding Mean Comparison)
Chapter 4: Data Analysis in Jupyter
- Preliminary Warmup Analysis
- Other Types of Datasets
- More Sophisticated Analysis
Chapter 5: Ideal Social and Soft Science Methods According to an Applied Scientist
- Missing Details of Structure and Procedure
- Areas of Weakness in the Provided Information
- Best Procedure or Ideal Method
Chapter 6: Understanding Your Data
- Kruskal-Wallis H test or ANOVA
- ACT Preparatory Program Efficacy
- Statistics and P-Values
- Mortality Rates
- One-Way ANOVA
Chapter 7: Relationships and Population Models
- Education Prediction
- Chi-Square vs Correlation
- Education Prediction Part 2
Chapter 8: Quantitative Research Elements
- Conducting a Quantitative Research Study
- The Literature Review
References

From the Author

I’ve seen firsthand how powerful statistical analysis can be, but also how traditional tools can create a barrier to entry for many who need to move their practice forward. I wrote this book for the social scientists, researchers, and students who have mastered their craft with legacy software and are ready for the next step. My goal is to provide a smooth, practical bridge from SPSS to the flexible, modern world of Python, empowering you to not only replicate your work but to expand your analytical capabilities with confidence. This is a guide for transition, built on a foundation of sound statistical practice.

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Make the leap to modern data analysis and unlock the full potential of your statistical skills.

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