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Statistics and Research Methods for Acute Care and General SurgeonsMultivariate Analysis

Statistics and Research Methods for Acute Care and General Surgeons: Multivariate Analysis [Clinical research is made by observation of clinical or biological events in small proportion of patients. From these observations we need to evaluate relationships among characteristics of our patients and outcomes in order to predict our outcomes. Regression models are statistics adopted to evaluate these relationships. In this chapter we will discuss most commonly adopted regression models, the linear and the logistic regressions. We also evaluate the differences among univariate and multiple regressions and the processes in building and interpreting these models.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Statistics and Research Methods for Acute Care and General SurgeonsMultivariate Analysis

Part of the Hot Topics in Acute Care Surgery and Trauma Book Series
Editors: Ceresoli, Marco; Abu-Zidan, Fikri M.; Staudenmayer, Kristan L.; Catena, Fausto; Coccolini, Federico
Allievi, Niccolò; Ceresoli, Marco
Statistics and Research Methods for Acute Care and General Surgeons — Dec 14, 2022
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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
ISBN
978-3-031-13817-1
Pages
77 –87
DOI
10.1007/978-3-031-13818-8_7
Publisher site
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Abstract

[Clinical research is made by observation of clinical or biological events in small proportion of patients. From these observations we need to evaluate relationships among characteristics of our patients and outcomes in order to predict our outcomes. Regression models are statistics adopted to evaluate these relationships. In this chapter we will discuss most commonly adopted regression models, the linear and the logistic regressions. We also evaluate the differences among univariate and multiple regressions and the processes in building and interpreting these models.]

Published: Dec 14, 2022

Keywords: Linear regression; Multiple regression; Univariate analysis; Multiple regression; Logistic regression

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