Modern Mathematical Statistics with Applications

ISBN-10: 0534404731

ISBN-13: 9780534404734

Edition: 2007

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Description: Many mathematical statistics texts are heavily oriented toward a rigorous mathematical development of probability and statistics, without emphasizing contemporary statistical practice. MODERN MATHEMATICAL STATISTICS WITH APPLICATIONS strikes a balance between mathematical foundations and statistical practice. Accomplished authors Jay Devore and Ken Berk first engage students with real-life problems and scenarios and then provide them with both foundational context and theory. This book follows the spirit of the Committee on the Undergraduate Program in Mathematics (CUPM) recommendation that every math student should study statistics and probability with an emphasis on data analysis.

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Book details

List price: $303.95
Copyright year: 2007
Publisher: Brooks/Cole
Publication date: 1/6/2006
Binding: Hardcover
Pages: 848
Size: 7.75" wide x 10.00" long x 1.25" tall
Weight: 2.882
Language: English

Jay Devore is Professor Emeritus of Statistics at California Polytechnic State University. He earned his undergraduate degree in Engineering Science from the University of California at Berkeley, spent a year at the University of Sheffield in England, and finished his Ph.D. in statistics at Stanford University. Jay previously taught at the University of Florida and at Oberlin College and has had visiting appointments at Stanford, Harvard, the University of Washington, New York University, and Columbia University. From 1998 to 2006, he served as Chair of the Statistics Department. In addition to this book, Jay has written several widely used engineering statistics texts and a book in applied mathematical statistics. He recently coauthored a text in probability and stochastic processes. He is the recipient of a distinguished teaching award from Cal Poly, is a Fellow of the American Statistical Association , and has served several terms as an Associate Editor of the "Journal of the American Statistical Association." In his spare time, he enjoys reading, cooking and eating good food, tennis, and travel to faraway places. He is especially proud of his wife, Carol, a retired elementary school teacher, his daughter Allison, who has held several high-level positions in nonprofit organizations in Boston and New York City, and his daughter Teresa, an ESL teacher in New York City.

Jay Devore received a B.S. in Engineering Science from the University of California, Berkeley, and a Ph.D. in Statistics from Stanford University. He previously taught at the University of Florida and Oberlin College, and has had visiting positions at Stanford, Harvard, the University of Washington, New York University, and Columbia. He has been at California Polytechnic State University, San Luis Obispo, since 1977, where he was chair of the Department of Statistics for seven years and recently achieved the exalted status of Professor Emeritus.Jay has previously authored or coauthored �ve other books, including Probability and Statistics for Engineering and the Sciences, which won a McGuffey Longevity Award from the Text and Academic Authors Association for demonstrated excellence over time . He is a Fellow of the American Statistical Association, has been an associate editor for both the Journal of the American Statistical Association and The American Statistician, and received the Distinguished Teaching Award from Cal Poly in 1991. His recreational interests include reading, playing tennis, traveling, and cooking and eating good food.Ken Berk has a B.S. in Physics from Carnegie Tech (now Carnegie Mellon) and a Ph.D. in Mathematics from the University of Minnesota. He is Professor Emeritus of Mathematics at Illinois State University and a Fellow of the American Statistical As­sociation. He founded the Software Reviews section of The American Statistician and edited it for six years. He served as secretary/treasurer, program chair, and chair of the Statistical Computing Section of the American Statistical Association, and he twice co-chaired the Interface Symposium, the main annual meeting in statistical computing. His published work includes papers on time series, statistical computing, regression analysis, and statistical graphics, as well as the book Data Analysis with Microsoft Excel (with Patrick Carey).

Overview And Descriptive Statistics
Introduction
Populations, Samples, and Processes
Pictorial and Tabular Methods in Descriptive Statistics
Measures of Location
Measures of Variability
Probability
Introduction
Sample Spaces and Events
Axioms, Interpretations, and Properties of Probability
Counting Techniques
Conditional Probability
Independence
Discrete Random Variables And Probability Distributions
Introduction
Random Variables
Probability Distributions for Discrete Random Variables
Expected Values of Discrete Random Variables
Moments and Moment Generating Functions
The Binomial Probability Distribution
The Hypergeometric and Negative Binomial Distributions
The Poisson Probability Distribution
Continuous Random Variables And Probability Distributions
Introduction
Probability Density Functions and Cumulative Distribution Functions
Expected Values and Moment Generating Functions
The Normal Distribution
The Gamma Distribution and Its Relatives
Other Continuous Distributions
Probability Plots
Transformations of a Random Variable
Joint Probability Distributions
Introduction
Jointly Distributed Random Variables
Expected Values, Covariance, and Correlation
Conditional Distributions
Transformations of Random Variables
Order Statistics
Statistics And Sampling Distributions
Introduction
Statistics and Their Distributions
The Distribution of the Sample Mean
The Distribution of a Linear Combination
Distributions Based on a Normal Random Sample
Appendix
Point Estimation
Introduction
Some General Concepts of Point Estimation
Methods of Point Estimation
Sufficiency
Information and Efficiency
Statistical Intervals Based On A Single Sample
Introduction
Basic Properties of Confidence Intervals
Large-Sample Confidence Intervals for a Population Mean and Proportion
Intervals Based on a Normal Population Distribution
Confidence Intervals for the Variance and Standard Deviation of a Normal Population
Bootstrap Confidence Intervals
Tests Of Hypotheses Based On A Single Sample
Introduction
Hypotheses and Test Procedures
Tests About a Population Mean
Tests Concerning a Population Proportion
P-Values
Some Comments on Selecting a Test Procedure
Inferences Based On Two Samples
Introduction
z Tests and Confidence Intervals for a Difference between Two Population Means
The Two-Sample t Test and Confidence Interval
Analysis of Paired Data
Inferences about Two Population Proportions
Inferences about Two Population Variances
Comparisons Using the Bootstrap and Permutation Methods
The Analysis Of Variance
Introduction
Single-Factor ANOVA
Multiple Comparisons in ANOVA
More on Single-Factor ANOVA
Two-Factor ANOVA with Kij =
Two-Factor ANOVA with Kij >
Regression And Correlation
Introduction
The Simple Linear and Logistic Regression Models
Estimating Model Parameters
Inferences about the Regression Coefficient ?-1??n Inferences Concerning ?�Y?�x*?n and the Prediction of Future Y Values
Correlation
Aptness of the Model and Model Checking
Multiple Regression Analysis
Regression with Matrices
Goodness-Of-Fit Tests And Categorical Data Analysis
Introduction
Goodness-of-Fit Tests When Category Probabilities Are Completely Specified
Goodness-of-Fit Tests for Composite Hypotheses
Two-Way Contingency Tables
Alternative Approaches To Inference
Introduction
The Wilcoxon Signed-Rank Test
The Wilcoxon Rank-Sum Test
Distribution-Free Confidence Intervals
Bayesian Methods
Sequential Methods
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