Book details

Other books

Other people who viewed this bought
Showing items 1 to 10 of 10

 

Full description | Reviews | Bibliographic data

  • Full bibliographic data for An Introduction to Mathematical Statistics and Its Applications

    Title
    An Introduction to Mathematical Statistics and Its Applications
    Authors and contributors
    By (author) Richard J. Larsen, By (author) Morris L. Marx
    Physical properties
    Format: Hardback
    Number of pages: 790
    Width: 184 mm
    Height: 238 mm
    Thickness: 34 mm
    Weight: 1,320 g
    Audience
    College/higher education
    Professional and scholarly
    Language
    English
    ISBN
    ISBN 13: 9780139223037
    ISBN 10: 0139223037
    Classifications
    BICMainSubject: PBW
    Dewey: 519.5
    Nielsen BookScan Product Class: S7.8
    BICMainSubject: PBT
    BISAC category code: BUS061000
    BISAC category code: MAT029000
    Edition
    3
    Edition statement
    3 Revised ed of US ed
    Illustrations note
    Illustrations, ports.
    Publisher
    Pearson Education (US)
    Imprint name
    Pearson Education (US)
    Publication date
    07 June 2000
    Publication City/Country
    Upper Saddle River/US
    Main description
    Using high-quality, real-world case studies and examples, this introduction to mathematical statistics shows how to use statistical methods and when to use them. This book can be used as a brief introduction to design of experiments. This successful, calculus-based book of probability and statistics, was one of the first to make real-world applications an integral part of motivating discussion. The number of problem sets has increased in all sections. Some sections include almost 50% new problems, while the most popular case studies remain. For anyone needing to develop proficiency with Mathematical Statistics.
    Table of contents
    (NOTE: Each chapter except Ch. 1 begins with an Introduction.) 1. Introduction. A Brief History. Some Examples. A Chapter Summary. 2. Probability. Sample Spaces and the Algebra of Sets. The Probability Function. Discrete Probability Functions. Continuous Probability Functions. Conditional Probability. Independence. Repeated Independent Trials. Combinatorics. Combinatorial Probability. 3. Random Variables. The Probability Density Function. The Hypergeometric and Binomial Distributions. The Cumulative Distribution Function. Joint Densities. Independent Random Variables. Combining and Transforming Random Variables. Order Statistics. Conditional Densities. Expected Values. Properties of Expected Values. The Variance. Properties of Variances. Chebyshev's Inequality. Higher Moments. Moment-Generating Functions. Appendix 3.A.1: MINITAB Applications. 4. Special Distributions. The Poisson Distribution. The Normal Distribution. The Geometric Distribution. The Negative Binomial Distribution. The Gamma Distribution. Appendix 4.A.1: MINITAB Applications. Appendix 4.A.2: A Proof of the Central Limit Theorem. 5. Estimation. Estimating Parameters: The Method of Maximum Likelihood and the Method of Moments. Interval Estimation. Properties of Estimators. Minimum-Variance Estimators: The Cramer-Rao Lower Bound. Sufficiency. Consistency. Appendix 5.A.1: MINITAB Applications. 6. Hypothesis Testing. The Decision Rule. Testing Binomial Data--H0: p = p 0. Type I and Type II Errors. A Notion of Optimality: The Generalized Likelihood Ratio. 7. The Normal Distribution. Point Estimates for !m and !s2. The !c2 Distribution; Inferences about !s2. The F and t Distributions. Drawing Inferences about !m. Appendix 7.A.1: MINITAB Applications. Appendix 7.A.2: Some Distribution Results for Y and S 2. Appendix 7.A.3: A Proof of Theorem 7.3.5. A Proof That the One-Sample t Test Is a GLRT. 8. Types of Data: A Brief Overview. Classifying Data. 9. Two-Sample Problems. Testing H 0: !mx = !mY--The Two-Sample t Test. Testing H0: !s2x = !s2Y--The F Test. Binomial Data: Testing H 0: px = py. Confidence Intervals for the Two-Sample Problem. Appendix 9.A.1: A Derivation of the Two-Sample t Test (A Proof of Theorem 9.2.2.). Appendix 9.A.2: Power Calculations for a Two-Sample t Test. Appendix 9.A.3: MINITAB Applications. 10. Goodness-of-Fit Tests. The Multinomial Distribution. Goodness-of-Fit Tests: All Parameters Known. Goodness-of-Fit Tests: Parameters Unknown. Contingency Tables. Appendix 10.A.1: MINITAB Applications. 11. Regression. The Method of Least Squares. The Linear Model. Covariance and Correlation. The Bivariate Normal Distribution. Appendix 11.A.1: MINITAB Applications. Appendix 11.A.2: A Proof of Theorem 11.3.3. 12. The Analysis of Variance. The F Test. Multiple Comparisons: Tukey's Method. Testing Subhypotheses with Orthogonal Contrasts. Data Transformations. Appendix 12.A.1: MINITAB Applications. Appendix 12.A.2: A Proof of Theorem 12.2.2. Appendix 12.A.3: The Distribution of When H1 Is True. 13. Randomized Block Designs. The F Test for a Randomized Block Design. The Paired t Test. Appendix 13.A.1: MINITAB Applications. 14. Nonparametric Statistics. The Sign Test. The Wilcoxon Signed Rank Test. The Kruskal-Wallis Test. The Friedman Test. Appendix 14.A.1: MINITAB Applications. Appendix: Statistical Tables. Answers to Selected Odd-Numbered Questions. Bibliography. Index.