An Introduction to Decision Theory (Cambridge Introductions to Philosophy) 1st Edition
This introduction to decision theory offers comprehensive and accessible discussions of decision-making under ignorance and risk, the foundations of utility theory, the debate over subjective and objective probability, Bayesianism, causal decision theory, game theory, and social choice theory. No mathematical skills are assumed, and all concepts and results are explained in non-technical and intuitive as well as more formal ways. There are over 100 exercises with solutions, and a glossary of key terms and concepts. An emphasis on foundational aspects of normative decision theory (rather than descriptive decision theory) makes the book particularly useful for philosophy students, but it will appeal to readers in a range of disciplines including economics, psychology, political science and computer science.
About the Author
Martin Peterson is Associate Professor of Philosophy at the Royal Institute of Technology, Sweden. He is author of Non-Bayesian Decision Theory (2008).
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Using Multivariate Statistics, 6th edition provides advanced undergraduate as well as graduate students with a timely and comprehensive introduction to today’s most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. This text’s practical approach focuses on the benefits and limitations of applications of a technique to a data set — when, why, and how to do it.
Upon completing this book, readers should be able to:
- Learn to conduct numerous types of multivariate statistical analyses
- Find the best technique to use
- Understand Limitations to applications
- Learn how to use SPSS and SAS syntax and output
About the Author
Barbara Tabachnick is Professor Emerita of Psychology at California State University, Northridge, and co-author with Linda Fidell of Using Multivariate Statistics and Experimental Designs Using ANOVA. She has published over 70 articles and technical reports and participated in over 50 professional presentations, many invited. She currently presents workshops in computer applications in univariate and multivariate data analysis and consults in a variety of research areas, including professional ethics in and beyond academia, effects of such factors as age and substances on driving and performance, educational computer games, effects of noise on annoyance and sleep, and fetal alcohol syndrome. She is the recipient of the 2012 Western Psychological Association Lifetime Achievement Award.
Introducing the concepts and methods of modern statistics with an emphasis on computer assisted data analysis, the book focuses on interpretation of results rather than their computation. Review of probability, collecting data, summarizing and exploring data, sampling distributions of statistics, basic concepts of inference, linear regression and correlation, analysis of single factor and multifactor Experiments.
About the Author
Sheldon M. Ross is a professor in the Department of Industrial Engineering and Operations Research at the University of Southern California. He received his Ph.D. in statistics at Stanford University in 1968. He has published many technical articles and textbooks in the areas of statistics and applied probability. Among his texts are A First Course in Probability, Introduction to Probability Models, Stochastic Processes, and Introductory Statistics. Professor Ross is the founding and continuing editor of the journal Probability in the Engineering and Informational Sciences, the Advisory Editor for International Journal of Quality Technology and Quantitative Management, and an Editorial Board Member of the Journal of Bond Trading and Management. He is a Fellow of the Institute of Mathematical Statistics and a recipient of the Humboldt US Senior Scientist Award.