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Mathematical Statistics with Applications in R,Used
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Mathematical Statistics with Applications in R, Second Edition, offers a modern calculusbased 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, MetropolisHastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of realworld applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a stepbystep 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, realworld 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. Stepbystep procedure to solve real problems, making the topic more accessible Exercises blend theory and modern applications Practical, realworld 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
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