Statistical Inference

Statistical inference is the method in which a researcher draws conclusions from research data which is subject to random variation. The end goal of statistical inference is to determine what is to be done next with the data set. Statistical Inference is a text that is ideally suited for students in their first year of graduate studies who have a firm footing in their understanding of mathematical concepts. The text builds on the basic theories of probability, using definitions, techniques, and concepts which are statistical and naturally extend from previous concepts. Students can use this text in two ways. They can focus on the practical aspects of the theory, understand basic concepts, and derive statistical procedures which could be used for different situations. Or, students can focus on formally optimizing their research investigations. This second edition of the text has several salient features. It contains new material on random number-generation, bootstrapping, simulation methods, p-values, EM algorithm, and robustness. The authors have also included new topics on Logistic Regression and Robust Regression. The material has been restructured for the purpose of clarity. Updated exercises and key features are also included. The text contains 12 chapters. The topics covered by these chapters are Probability Theory, Multiple Random Variables, Common Families of Distributions, Random Sample Properties, Data Reduction Principles, Interval Estimation, Point Estimation, Hypothesis Testing, Asymptotic Evaluations, Regression and Variance Analysis, and Regression Models. Statistical Inference was published in 2007.…