Biostatistics for Epidemiology and Public Health Using R
Nonfiction, Health & Well Being, Medical, Medical Science, Biotechnology, Ailments & Diseases, Infectious Diseases, Epidemiology, Reference, Public Health
| Author: | Bertram K.C. Chan, PhD | ISBN: | 9780826110268 |
| Publisher: | Springer Publishing Company | Publication: | November 5, 2015 |
| Imprint: | Springer Publishing Company | Language: | English |
| Author: | Bertram K.C. Chan, PhD |
| ISBN: | 9780826110268 |
| Publisher: | Springer Publishing Company |
| Publication: | November 5, 2015 |
| Imprint: | Springer Publishing Company |
| Language: | English |
Since it first appeared in 1996, the open-source programming language R has become increasingly popular as an environment for statistical analysis and graphical output. This is the first textbook to present classical biostatistical analysis for epidemiology and related public health sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses a step-by-step approach to building skills. The text encompasses biostatistics from basic descriptive and quantitative statistics to survival analysis and missing data analysis in epidemiology. Illustrative examples, including real-life research problems drawn from such areas as nutrition, environmental health, and behavioral health, engage students and reinforce the understanding of biostatistics and how to perform these analyses using R.
Since it first appeared in 1996, the open-source programming language R has become increasingly popular as an environment for statistical analysis and graphical output. This is the first textbook to present classical biostatistical analysis for epidemiology and related public health sciences to students using the R language. Based on the assumption that readers have minimal familiarity with statistical concepts, the author uses a step-by-step approach to building skills. The text encompasses biostatistics from basic descriptive and quantitative statistics to survival analysis and missing data analysis in epidemiology. Illustrative examples, including real-life research problems drawn from such areas as nutrition, environmental health, and behavioral health, engage students and reinforce the understanding of biostatistics and how to perform these analyses using R.
