Statistical analytics for health data science with SAS and R
Author(s)
Bibliographic Information
Statistical analytics for health data science with SAS and R
（Chapman & Hall/CRC biostatistics series）
CRC Press, 2023
- : hbk
Available at 2 libraries
Note
"A Chapman & Hall book"
Includes bibliographical references and index
Description and Table of Contents
Description
1) Extensive compilation of commonly used statistical methods from fundamental to advanced
level, which are essential for applied data scientists and practitioners in data science
2) Straightforward explanations of the collected statistical theory and models
3) Compilation of a variety of publicly available data
4) Illustration of data analytics using commonly used statistical software of SAS/R (with
SPSS/Stata as online supplementary materials)
5) Data and computer programs are available for readers to replicate and implement the new
methods for better understand the book contents and for future applications
6) Handbook for data scientists and applied statisticians.
Table of Contents
1. Sampling and Data Collection 2. Measures of Tendency, Spread, Relative Standing, Association, Belief 3. Statistical Modeling of Mean of Continuous and Mean of Binary Outcomes 4. Modeling of Continuous and Binary Outcomes with Factors: One-way and Two-way ANOVA Models 5. Statistical Modeling of Continuous Outcomes with Continuous Explanatory Factors Linear Regression Models 6. Modeling Continuous Responses with Categorical and Continuous Covariates: One-Way Analysis of Covariance (ANCOVA) 7. Statistical Modeling of Binary Outcome with One or More Covariates: Standard Logistic Regression Model 8. Generalized Linear Models 9. Modeling Repeated Continuous Observations using GEE 10. Modeling for Correlated Continuous Responses with Random-Effects 11. Modeling Correlated Binary Outcomes through Hierarchical Logistic Regression Models
by "Nielsen BookData"