Preface
Contained in this volume are the solutions to homework problems in the Computational Probability and Statistics book, master version.
0.1 Book Structure and How to Use It
This solutions manual is set up to match the structure of the accompanying book.
The learning outcomes for this course are to use computational and mathematical statistical/probabilistic concepts for:
- Developing probabilistic models.
- Developing statistical models for description, inference and prediction.
- Advancing practical and theoretical analytic experience and skills.
0.2 Packages
These notes make use of the following packages in R knitr (Xie 2023), rmarkdown (Allaire et al. 2023), mosaic (Pruim, Kaplan, and Horton 2022), mosaicCalc (Kaplan, Pruim, and Horton 2022), tidyverse (Wickham 2023), ISLR (James et al. 2021), vcd (Meyer, Zeileis, and Hornik 2023), ggplot2 (Wickham et al. 2023), MASS (Ripley 2023), openintro (Çetinkaya-Rundel et al. 2022), broom (Robinson, Hayes, and Couch 2023), infer (Bray et al. 2022), kableExtra (Zhu 2021), DT (Xie, Cheng, and Tan 2023).
This book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.