SAS for epidemiologists : applications and methods

This comprehensive text covers the use of SAS for epidemiology and public health research. Developed with students in mind and from their feedback, the text addresses this material in a straightforward manner with a multitude of examples. It is directly applicable to students and researchers in the...

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主要作者: DiMaggio, Charles, 19..-
格式: Livre numérique
語言:Anglais
出版: New York, NY : Springer New York : Imprint: Springer [20..].
Cham : Springer Nature
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Edition sous un autre format:• SAS for Epidemiologists, Texte imprimé, 9781461448532
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505 1 |a Introduction The SAS Environment Working with SAS Data Preliminary Procedures Manipulating Data Descriptive Statistics Histograms and Plots Categorical Data Analysis I and II Cleaning and Assessing Continuous Data using MEANS ANOVA Correlation Linear Regression Regression Diagnostics Solutions 
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520 |a This comprehensive text covers the use of SAS for epidemiology and public health research. Developed with students in mind and from their feedback, the text addresses this material in a straightforward manner with a multitude of examples. It is directly applicable to students and researchers in the fields of public health, biostatistics and epidemiology. Through a hands on approach to the use of SAS for a broad number of epidemiologic analyses, readers learn techniques for data entry and cleaning, categorical analysis, ANOVA, and linear regression and much more. Exercises utilizing real-world data sets are featured throughout the book. SAS screen shots demonstrate the steps for successful programming.SAS (Statistical Analysis System) is an integrated system of software products provided by the SAS institute, which is headquartered in California. It provides programmers and statisticians the ability to engage in many sophisticated statistical analyses and data retrieval and mining exercises. SAS is widely used in the fields of epidemiology and public health research, predominately due to its ability to reliably analyze very large administrative data sets, as well as more commonly encountered clinical trial and observational research data.    
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