Mathematical Statistics with Applications in R, Second Edition
by Kandethody M. Ramachandran, Chris P. Tsokos
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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.
This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students.
Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies.
- Step-by-step procedure to solve real problems, making the topic more accessible
- Exercises blend theory and modern applications
- Practical, real-world chapter projects
- Provides an optional section in each chapter on using Minitab, SPSS and SAS commands
- Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods
Discussion questions for Mathematical Statistics with Applications in R, Second Edition
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- 1
How does the integration of R programming alongside traditional mathematical statistics change the way you conceptualize abstract statistical theory compared to learning it purely by hand?
- 2
In what ways does the transition from classical inferential methods to modern computational techniques like the bootstrap and MCMC alter your intuition about probability and data?
- 3
Reflecting on the real-world chapter projects, how well do these applied scenarios bridge the gap between textbook formulas and the messy, uncertain nature of actual data from your own field or daily life?
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