Modern Survey Analysis - Paczkowski, Walter R.
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This book develops survey data analysis tools in Python, to create and analyze cross-tab tables and data visuals, weight data, perform hypothesis tests, and handle special survey questions such as Check-all-that-Apply. In addition, the basics of Bayesian data analysis and its Python implementation are presented. Since surveys are widely used as the primary method to collect data, and ultimately information, on attitudes, interests, and opinions of customers and constituents, these tools are vital for private or public sector policy decisions.
As a compact volume, this book uses case studies…mehr

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Produktbeschreibung
This book develops survey data analysis tools in Python, to create and analyze cross-tab tables and data visuals, weight data, perform hypothesis tests, and handle special survey questions such as Check-all-that-Apply. In addition, the basics of Bayesian data analysis and its Python implementation are presented. Since surveys are widely used as the primary method to collect data, and ultimately information, on attitudes, interests, and opinions of customers and constituents, these tools are vital for private or public sector policy decisions.

As a compact volume, this book uses case studies to illustrate methods of analysis essential for those who work with survey data in either sector. It focuses on two overarching objectives:
Demonstrate how to extract actionable, insightful, and useful information from survey data; andIntroduce Python and Pandas for analyzing survey data.

Autorenporträt
Walter R. Paczkowski, PhD, has worked at AT&T, AT&T Bell Labs, and AT&T Labs. He founded Data Analytics Corp., a statistical consulting company, in 2001. Dr. Paczkowski is also a part-time lecturer of economics at Rutgers University. He is the author of Business Analytics: Data Science for Business Problems (2022), Deep Data Analytics for New Product Development  (2020), Pricing Analytics: Models and Advanced Quantitative Techniques for Product Pricing (2018), and Market Data Analysis Using JMP (2016).