Mathematical Statistics and Data Analysis (with CD Data Sets)

Name: Mathematical Statistics and Data Analysis (with CD Data Sets)
Availability: InStock
ISBN: 9780534399429

ISBN-10: 0534399428

ISBN-13: 9780534399429

Edition: 3rd 2007 (Revised)

Authors: John Rice

List price: $231.95

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

Sell

Get cash fast!

Semester Rental: $116.72 Due date: 8/14/2024

Rental notice: supplementary materials (access codes, CDs, etc.) are not guaranteed with rental orders.

Buy used: $45.13

Buy new: $92.51

Rent eBooks

180 day rental: $56.99 Expiration date: 10/15/2024

365 day rental: $78.49 Expiration date: 4/18/2025

Marketplace

5 new & used from $15.56

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

This is the first text in a generation to re-examine the purpose of the mathematical statistics course. The book's approach interweaves traditional topics with data analysis and reflects the use of the computer with close ties to the practice of statistics. The author stresses analysis of data, examines real problems with real data, and motivates the theory. The book's descriptive statistics, graphical displays, and realistic applications stand in strong contrast to traditional texts that are set in abstract settings.

Book details

List price: $231.95
Edition: 3rd
Copyright year: 2007
Publisher: Brooks/Cole
Publication date: 4/28/2006
Binding: Mixed Media
Pages: 688
Size: 7.64" wide x 9.45" long x 1.22" tall
Weight: 2.948
Language: English

John Rice is a wildlife artist and is the illustrator of How Dogs Came from Wolves, On the Trail of the Komodo Dragon, On Top of Mount Everest, and What Happened to the Mammoths? He lives in Mount Kisco, New York.



Preface



Probability



Introduction



Sample Spaces



Probability Measures



Computing Probabilities: Counting Methods



The Multiplication Principle



Permutations and Combinations



Conditional Probability



Independence



Concluding Remarks



Problems



Random Variables



Discrete Random Variables



Bernoulli Random Variables



The Binomial Distribution



The Geometric and Negative Binomial Distributions



The Hypergeometric Distribution



The Poisson Distribution



Continuous Random Variables



The Exponential Density



The Gamma Density



The Normal Distribution



The Beta Density



Functions of a Random Variable



Concluding Remarks



Problems



Joint Distributions



Introduction



Discrete Random Variables



Continuous Random Variables



Independent Random Variables



Conditional Distributions



The Discrete Case



The Continuous Case



Functions of Jointly Distributed Random Variables



Sums and Quotients



The General Case



Extrema and Order Statistics



Problems



Expected Values



The Expected Value of a Random Variable



Expectations of Functions of Random Variables



Expectations of Linear Combinations of Random Variables



Variance and Standard Deviation



A Model for Measurement Error



Covariance and Correlation



Conditional Expectation and Prediction



Definitions and Examples



Prediction



The Moment-Generating Function



Approximate Methods



Problems



Limit Theorems



Introduction



The Law of Large Numbers



Convergence in Distribution and the Central Limit Theorem



Problems



Distributions Derived from the Normal Distribution



Introduction



x[superscript 2], t, and F Distributions



The Sample Mean and the Sample Variance



Problems



Survey Sampling



Introduction



Population Parameters



Simple Random Sampling



The Expectation and Variance of the Sample Mean



Estimation of the Population Variance



The Normal Approximation to the Sampling Distribution of X



Estimation of a Ratio



Stratified Random Sampling



Introduction and Notation



Properties of Stratified Estimates



Methods of Allocation



Concluding Remarks



Problems



Estimation of Parameters and Fitting of Probability Distributions



Introduction



Fitting the Poisson Distribution to Emissions of Alpha Particles



Parameter Estimation



The Method of Moments



The Method of Maximum Likelihood



Maximum Likelihood Estimates of Multinomial Cell Probabilities



Large Sample Theory for Maximum Likelihood Estimates



Confidence Intervals from Maximum Likelihood Estimates



The Bayesian Approach to Parameter Estimation



Further Remarks on Priors



Large Sample Normal Approximation to the Posterior



Computational Aspects



Efficiency and the Cramer-Rao Lower Bound



An Example: The Negative Binomial Distribution



Sufficiency



A Factorization Theorem



The Rao-Blackwell Theorem



Concluding Remarks



Problems



Testing Hypotheses and Assessing Goodness of Fit



Introduction



The Neyman-Pearson Paradigm



Specification of the Significance Level and the Concept of a p-value



The Null Hypothesis



Uniformly Most Powerful Tests



The Duality of Confidence Intervals and Hypothesis Tests



Generalized Likelihood Ratio Tests



Likelihood Ratio Tests for the Multinomial Distribution



The Poisson Dispersion Test



Hanging Rootograms



Probability Plots



Tests for Normality



Concluding Remarks



Problems



Summarizing Data



Introduction



Methods Based on the Cumulative Distribution Function



The Empirical Cumulative Distribution Function



The Survival Function



Quantile-Quantile Plots



Histograms, Density Curves, and Stem-and-Leaf Plots



Measures of Location



The Arithmetic Mean



The Median



The Trimmed Mean



M Estimates



Comparison of Location Estimates



Estimating Variability of Location Estimates by the Bootstrap



Measures of Dispersion



Boxplots



Exploring Relationships with Scatterplots



Concluding Remarks



Problems



Comparing Two Samples



Introduction



Comparing Two Independent Samples



Methods Based on the Normal Distribution



Power



A Nonparametric Method-The Mann-Whitney Test



Bayesian Approach



Comparing Paired Samples



Methods Based on the Normal Distribution



A Nonparametric Method-The Signed Rank Test



An Example-Measuring Mercury Levels in Fish



Experimental Design



Mammary Artery Ligation



The Placebo Effect



The Lanarkshire Milk Experiment



The Portacaval Shunt



FD&C Red No. 40



Further Remarks on Randomization



Observational Studies, Confounding, and Bias in Graduate Admissions



Fishing Expeditions



Concluding Remarks



Problems



The Analysis of Variance



Introduction



The One-Way Layout



Normal Theory; the F Test



The Problem of Multiple Comparisons



A Nonparametric Method-The Kruskal-Wallis Test



The Two-Way Layout



Additive Parametrization



Normal Theory for the Two-Way Layout



Randomized Block Designs



A Nonparametric Method-Friedman's Test



Concluding Remarks



Problems



The Analysis of Categorical Data



Introduction



Fisher's Exact Test



The Chi-Square Test of Homogeneity



The Chi-Square Test of Independence



Matched-Pairs Designs



Odds Ratios



Concluding Remarks



Problems



Linear Least Squares



Introduction



Simple Linear Regression



Statistical Properties of the Estimated Slope and Intercept



Assessing the Fit



Correlation and Regression



The Matrix Approach to Linear Least Squares



Statistical Properties of Least Squares Estimates



Vector-Valued Random Variables



Mean and Covariance of Least Squares Estimates



Estimation of [gamma superscript 2]



Residuals and Standardized Residuals



Inference about [beta]



Multiple Linear Regression-An Example



Conditional Inference, Unconditional Inference, and the Bootstrap



Local Linear Smoothing



Concluding Remarks



Problems



Common Distributions



Tables


Bibliography


Answers to Selected Problems


Author Index


Applications Index


Subject Index