Statistical Analytics for Health Data Science with SAS and R, Two-Volume Set
€ 284,90
Informação do Produto
| ISBN | 9781041089872 |
|---|---|
| Autores | Ding-Geng Chen, Jeffrey Wilson, Karl E. Peace |
| Editora | Taylor & Francis Ltd |
| Formato | Outros |
| Número de páginas | 510 |
| Peso (kg) | 1,090 |
| Data de Publicação | 14/11/2025 |
| Preço (iva excl.) | € 268,77 |
| Imposto | € 16,13 |
| Preço (iva inc.) | € 284,90 |
| Disponibilidade | 3 unidades em armazém externo |
Descrição do Produto
Statistical Analytics for Health Data Science with SAS and R, Two-Volume Set compiles fundamental statistical principles with advanced analytical techniques and covers a wide range of statistical methodologies including models for longitudinal data with time-dependent covariates, multi-membership mixed-effects models, statistical modeling of survival data, Bayesian statistics, joint modeling of longitudinal and survival data, nonlinear regression, statistical meta-analysis, spatial statistics, structural equation modeling, latent growth curve modeling, causal inference and propensity score analysis. With an emphasis on real-world applications, the books integrate publicly available health datasets and provide case studies from a variety of health applications demonstrating how statistical methods can be applied to solve critical problems in health science. To support hands-on learning, they offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models. Statistical Analytics for Health Data Science with SAS and R has been expanded from eleven chapters to twenty-three chapters in two textbooks and is intended for data scientists and applied statisticians while also being useful as a comprehensive reference for graduate students, academic researchers and public health professionals that will help them gain expertise in advance data-driven decision-making and contribute to evidence-based health research. Key Features:Extensive compilation of commonly used statistical methods from fundamental to advanced levelStraightforward explanations of the collected statistical theory and modelsIllustration of data analytics using commonly used statistical software of SAS/R and real health dataHandbook for data scientists and applied statisticians in health data science