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Presenting statistical results effectively

By: Andersen, Robert.
Contributor(s): Armstrong II, David A.
Publisher: Los Angeles : Sage, 2022Description: xxvi, 424 p. ; ill., (some col.), 25 cm.ISBN: 9781446269817.Subject(s): Akaike's information criterion | Baysian Information ccriterion | Binary regression models | Clarke test | Dummt coding | Effect coding | Fitted curve | GDP | Interaction effects | Knuth binning method | Linear models | Multinomial logistic regression models | Nested models | Ordered models | Regression modelsDDC classification: 001.4226 Summary: Perfect for any statistics student or researcher, this book offers hands-on guidance on how to interpret and discuss your results in a way that not only gives them meaning, but also achieves maximum impact on your target audience. No matter what variables your data involves, it offers a roadmap for analysis and presentation that can be extended to other models and contexts. Focused on best practices for building statistical models and effectively communicating their results, this book helps you : Find the right analytic and presentation techniques for your type of data ; Understand the cognitive processes involved in decoding information ; Assess distributions and relationships among variables ; Know when and how to choose tables or graphs ; Build, compare, and present results for linear and non-linear models ; Work with univariate, bivariate, and multivariate
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Books 001.4226 AND (Browse shelf) Available 034154

Includes bibliographical references and index.

Perfect for any statistics student or researcher, this book offers hands-on guidance on how to interpret and discuss your results in a way that not only gives them meaning, but also achieves maximum impact on your target audience. No matter what variables your data involves, it offers a roadmap for analysis and presentation that can be extended to other models and contexts. Focused on best practices for building statistical models and effectively communicating their results, this book helps you : Find the right analytic and presentation techniques for your type of data ; Understand the cognitive processes involved in decoding information ; Assess distributions and relationships among variables ; Know when and how to choose tables or graphs ; Build, compare, and present results for linear and non-linear models ; Work with univariate, bivariate, and multivariate

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