5 edition of Statistical theory and inference in research found in the catalog.
|Statement||T.A. Bancroft, Chien-Pai Han.|
|Series||Statistics, textbooks and monographs ;, v. 39|
|Contributions||Han, Chien-Pai, 1936-, Anderson, R. L. 1915-|
|LC Classifications||QA276 .B2718 1981|
|The Physical Object|
|Pagination||xiv, 372 p. ;|
|Number of Pages||372|
|LC Control Number||81015205|
This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The ability to formulate abstract concepts and draw conclusions from data is fundamental to mastering statistics. Aspects of Statistical Inference equips advanced undergraduate and graduate students with a comprehensive grounding in statistical inference, including nonstandard topics such as robustness, randomization, and finite population.
Statistical inference involves hypothesis testing (evaluating some idea about a population using a sample) and estimation (estimating the value or potential range of values of some characteristic of the population based on that of a sample). Archaeologists were relatively slow to realize the analytical potential of statistical theory and methods. The book is designed for students in statistics at the master level. It focuses on problem solving in the field of statistical inference and should be regarded as a complement to text books such as Wackerly et al , Mathematical Statistics with Applications or Casella & Berger , Statistical Inference.
Statistics for Social Scientists Quantitative social science research: 1 Find a substantive question 2 Construct theory and hypothesis 3 Design an empirical study and collect data 4 Use statistics to analyze data and test hypothesis 5 Report the results No study in the social sciences is perfect Use best available methods and data, but be aware of limitations. This book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and .
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“This book describes the most important aspects of subjective classical statistical theory and inference similar to the treatment in Rohatgi. The book can be considered as a guide for teachers and students in the first or second courses in classical statistical methods .Cited by: This book provides an introduction to probability theory, statistical inference, and statistical modeling for social science researchers and Ph.D.
students. Focusing on the connection between statistical procedures and social science theory, Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships Cited by: 8.
An appropriate list of references is given at the end of the book. It is a welcome addition to the overcrowded statistical market and can be easily ranked as one of the best books, if not the best, on statistical inference (theory and methods).” (D.
Chopra, Mathematical Reviews, August, )Cited by: This text is for a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square err Statistical Theory and Inference | Springer for Research & Development.
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Statistical theory and inference in research Item Preview remove-circle Statistical theory in research / by R.L. Anderson and T.A. Bancroft. Pages: Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics Book ) - Kindle edition by Boos, Dennis D., Stefanski, L A.
Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics Book ).5/5(3).
This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer. An appropriate list of references is given at the end of the book. It is a welcome addition to the overcrowded statistical market and can be easily ranked as one of the best books, if not the best, on statistical inference (theory and methods).” (D.
Chopra, Mathematical Reviews, August, ). In May of we organized an international research colloquium on foundations of probability, statistics, and statistical theories of science at the University of Western Ontario. During the past four decades there have been striking formal advances in our understanding of logic, semantics and algebraic structure in probabilistic and.
Additional Physical Format: Online version: Bancroft, T.A. (Theodore Alfonso), Statistical theory and inference in research. New York: M. Dekker, © “This book describes the most important aspects of subjective classical statistical theory and inference similar to the treatment in Rohatgi.
The book can be considered as a guide for teachers and students in the first or second courses in classical statistical methods .Pages: About the authors This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research.
It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics.
About the Book. This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics college student, and covers the topics typically covered in the first semester of such a : Brian Blais.
Statistical Inference and Related Topics, Volume 2 presents the proceedings of the Summer Research Institute on Statistical Inference for Stochastic Processes, held in Bloomingdale, Indiana on July 31 to August 9, This book focuses on the theory of statistical inference for stochastic processes.
This text is for a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sample theory, likelihood ratio tests and.
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David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology.
Anyone can suggest me one or more good books on Statistical Inference (estimators, UMVU estimators, hypothesis testing, UMP test, I like Theory of Statistics by Schervish. It covers both the classical and Bayesian theory, but does not slight either of them.
Provide details and share your research. Adopting a broad view of statistical inference, the text concentrates on what various techniques do, with mathematical proof kept to a minimum.
The approach is rigorous but accessible to final year undergraduates. Classical approaches to point estimation, hypothesis testing and interval estimation are all covered thoroughly with recent developments outlined.5/5(3).
This is definitely not my thing, but I thought I would mention a video I watched three times and will watch again to put it firmly in my mind. It described how the living cell works with very good animations presented. Toward the end of the vide.
Statistical inference is the process of using data analysis to deduce properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving is assumed that the observed data set is sampled from a larger population.
Inferential statistics can be contrasted with descriptive statistics.'Written as a series of tours and excursions, Deborah G.
Mayo's lively book revisits the foundations of statistical inference from a simple and clear premise: only trust results that pass `severe tests'. Her ideas can be thought of as a modern, more complete version of Popper's notion of falsifiability.Recent Bayesian accounts of theory-based inductive inference make explicit the link to formal statistical theory (Chater, ; Xu & Tenenbaum, b).
What is missing, or at least underemphasized, is the degree to which a shared commitment to statistical inference as a model for human inference provides a basis for contrasting theory-based and.