@article{1435702,
      recid = {1435702},
      author = {Chen, Ding-Geng, and Chen, Jenny K.,},
      title = {Statistical regression modeling with R : longitudinal and  multi-level modeling /},
      pages = {1 online resource},
      abstract = {This book provides a concise point of reference for the  most commonly used regression methods. It begins with  linear and nonlinear regression for normally distributed  data, logistic regression for binomially distributed data,  and Poisson regression and negative-binomial regression for  count data. It then progresses to these regression models  that work with longitudinal and multi-level data  structures. The volume is designed to guide the transition  from classical to more advanced regression modeling, as  well as to contribute to the rapid development of  statistics and data science. With data and computing  programs available to facilitate readers' learning  experience, Statistical Regression Modeling promotes the  applications of R in linear, nonlinear, longitudinal and  multi-level regression. All included datasets, as well as  the associated R program in packages nlme and lme4 for  multi-level regression, are detailed in Appendix A. This  book will be valuable in graduate courses on applied  regression, as well as for practitioners and researchers in  the fields of data science, statistical analytics, public  health, and related fields.},
      url = {http://library.usi.edu/record/1435702},
      doi = {https://doi.org/10.1007/978-3-030-67583-7},
}