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Modern Survey Analysis = Using Python for Deeper Insights /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Modern Survey Analysis/ by Walter R. Paczkowski.
Reminder of title:
Using Python for Deeper Insights /
Author:
Paczkowski, Walter R.
Description:
XXVI, 347 p. 226 illus., 221 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Statistics . -
Online resource:
https://doi.org/10.1007/978-3-030-76267-4
ISBN:
9783030762674
Modern Survey Analysis = Using Python for Deeper Insights /
Paczkowski, Walter R.
Modern Survey Analysis
Using Python for Deeper Insights /[electronic resource] :by Walter R. Paczkowski. - 1st ed. 2022. - XXVI, 347 p. 226 illus., 221 illus. in color.online resource.
1. Introduction -- 2. Understanding the structure of survey data -- 3. Shallow analyses of survey data -- 4. Deep analyses of survey data -- 5. Conclusion and wrap-up.
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; and Introduce Python and Pandas for analyzing survey data.
ISBN: 9783030762674
Standard No.: 10.1007/978-3-030-76267-4doiSubjects--Topical Terms:
1253516
Statistics .
LC Class. No.: QA276-280
Dewey Class. No.: 300.727
Modern Survey Analysis = Using Python for Deeper Insights /
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1. Introduction -- 2. Understanding the structure of survey data -- 3. Shallow analyses of survey data -- 4. Deep analyses of survey data -- 5. Conclusion and wrap-up.
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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 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; and Introduce Python and Pandas for analyzing survey data.
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