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Mathematical Statistics with Applications
ID: 173642
KM Ramachandran, Chris Tsokos
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Many students who do well in mathematics. Mathematical Statistics and Its Applications is a real problem. This is the problem solving problem in a logical manner.
This textbook provides a calculation of the text and their applications. It assumes no prescription. Most books are written with eleaborate coverage of probability. This creates a problem for non mathematics statistics and applications. The authors should be aware of the sound with theoretical explanations. As statistics is basically an interdisciplinary applied subject, many applied examples.
The book introduces you to other texts; such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo methods such as the Metropolis-algorithm, Metropolis-Hastings algorth and the Gibbs sampler.
▪ Step-by-step procedure to solve real problems, making the topic more accessible
▪ Exercises blend theory and modern applications
▪ Practical, real-world chapter projects
▪ Minitab, SPSS and SAS commands
▪ Student solutions manual, instructors manual and data disk available
Preface, Descriptive Statistics, Basic Concepts in Probability, Sampling Distributions, Point Estimation, Interval Estimation, Hypothesis Testing, Linear Regression Models, Design of Experiments, Analysis of variance, Bayesian Estimation and Inference, Nonparametric tests, Empirical Methods , Some issues in statistical applications- an overview, Appendices
This textbook provides a calculation of the text and their applications. It assumes no prescription. Most books are written with eleaborate coverage of probability. This creates a problem for non mathematics statistics and applications. The authors should be aware of the sound with theoretical explanations. As statistics is basically an interdisciplinary applied subject, many applied examples.
The book introduces you to other texts; such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo methods such as the Metropolis-algorithm, Metropolis-Hastings algorth and the Gibbs sampler.
▪ Step-by-step procedure to solve real problems, making the topic more accessible
▪ Exercises blend theory and modern applications
▪ Practical, real-world chapter projects
▪ Minitab, SPSS and SAS commands
▪ Student solutions manual, instructors manual and data disk available
Preface, Descriptive Statistics, Basic Concepts in Probability, Sampling Distributions, Point Estimation, Interval Estimation, Hypothesis Testing, Linear Regression Models, Design of Experiments, Analysis of variance, Bayesian Estimation and Inference, Nonparametric tests, Empirical Methods , Some issues in statistical applications- an overview, Appendices
173642
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KM Ramachandran, Chris Tsokos