What Makes Variables Random

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A01=Peter J. Veazie
advanced probability theory
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Age Group_Uncategorized
Age Subsets
Applied Researcher
Author_Peter J. Veazie
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Borel Sigma Algebra
Cambridge University
Category1=Non-Fiction
Category=PBT
Conditional Expectation
COP=United States
Cumulative Distribution Functions
Data Generating Process
Data Generating Process Parameter
Data Model
Delivery_Pre-order
Disjoint Collection
eq_isMigrated=2
eq_nobargain
hierarchical modeling approaches
Index Set
Language_English
Measurable Space
Measure theory
measure theory applications
measure-theoretic probability
measure-theoretic probability in research
Natural Data Sets
Negative Real Line
Nonnegative Measurable Function
Normalized Histogram
Outcome Set
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probability modeling techniques
Probability Space
PS=Active
Random Assignment
Random Effects Model
Random Variables
Research Cooperative System
social science statistics
softlaunch
Standard Random Effects Models
statistical inference concepts
Underlying Probability Space
Vice Versa

Product details

  • ISBN 9781498781084
  • Weight: 362g
  • Dimensions: 156 x 234mm
  • Publication Date: 03 May 2017
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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What Makes Variables Random: Probability for the Applied Researcher provides an introduction to the foundations of probability that underlie the statistical analyses used in applied research. By explaining probability in terms of measure theory, it gives the applied researchers a conceptual framework to guide statistical modeling and analysis, and to better understand and interpret results.

The book provides a conceptual understanding of probability and its structure. It is intended to augment existing calculus-based textbooks on probability and statistics and is specifically targeted to researchers and advanced undergraduate and graduate students in the applied research fields of the social sciences, psychology, and health and healthcare sciences.

Materials are presented in three sections. The first section provides an overall introduction and presents some mathematical concepts used throughout the rest of the text. The second section presents the basic structure of measure theory and its special case of probability theory. The third section provides the connection between a conceptual understanding of measure-theoretic probability and applied research. This section starts with a chapter on its use in understanding basic models and finishes with a chapter that focuses on more complicated problems, particularly those related to various types and definitions of analyses related to hierarchical modeling.

Peter Veazie, PhD, is an associate professor in health services research and policy at the University of Rochester. He is the Chief of the division of Health Policy and Outcomes Research and the Director of the Health Services Research and Policy graduate programs. Dr. Veazie’s research interests include the psychology of health care decision making, health outcomes, and statistical research methods.

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