Applied Statistics

Applied Statistics

by Dieter Rasch, Rob Verdooren, Jürgen Pilz
Released October 2019
Publisher(s): Wiley
ISBN: 9781119551522

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Book description

Instructs readers on how to use methods of statistics and experimental design with R software 

Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one's ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory.

Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott's Mathematical Statistics via that book's theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics. 

  • Offers a practical over theoretical approach to the subject of applied statistics
  • Provides a pre-experimental as well as post-experimental approach to applied statistics
  • Features classroom tested material
  • Applicable to a wide range of people working in experimental design and all empirical sciences
  • Includes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizes

Applied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others. 

Table of contents

  1. Cover
  2. Preface
    1. References
  3. 1 The R‐Package, Sampling Procedures, and Random Variables
    1. 1.1 Introduction
    2. 1.2 The Statistical Software Package R
    3. 1.3 Sampling Procedures and Random Variables
    4. References
  4. 2 Point Estimation
    1. 2.1 Introduction
    2. 2.2 Estimating Location Parameters
    3. 2.3 Estimating Scale Parameters
    4. 2.4 Estimating Higher Moments
    5. 2.5 Contingency Tables
    6. References
  5. 3 Testing Hypotheses – One‐ and Two‐Sample Problems
    1. 3.1 Introduction
    2. 3.2 The One‐Sample Problem
    3. 3.3 The Two‐Sample Problem
    4. References
  6. 4 Confidence Estimations – One‐ and Two‐Sample Problems
    1. 4.1 Introduction
    2. 4.2 The One‐Sample Case
    3. 4.3 The Two‐Sample Case
    4. References
  7. 5 Analysis of Variance (ANOVA) – Fixed Effects Models
    1. 5.1 Introduction
    2. 5.2 Planning the Size of an Experiment
    3. 5.3 One‐Way Analysis of Variance
    4. 5.4 Two‐Way Analysis of Variance
    5. 5.5 Three‐Way Classification
    6. References
  8. 6 Analysis of Variance – Models with Random Effects
    1. 6.1 Introduction
    2. 6.2 One‐Way Classification
    3. 6.3 Two‐Way Classification
    4. 6.4 Three‐Way Classification
    5. References
  9. 7 Analysis of Variance – Mixed Models
    1. 7.1 Introduction
    2. 7.2 Two‐Way Classification
    3. 7.3 Three‐Way Layout
    4. References
  10. 8 Regression Analysis
    1. 8.1 Introduction
    2. 8.2 Regression with Non‐Random Regressors – Model I of Regression
    3. 8.3 Models with Random Regressors
    4. References
  11. 9 Analysis of Covariance (ANCOVA)
    1. 9.1 Introduction
    2. 9.2 Completely Randomised Design with Covariate
    3. 9.3 Randomised Complete Block Design with Covariate
    4. 9.4 Concluding Remarks
    5. References
  12. 10 Multiple Decision Problems
    1. 10.1 Introduction
    2. 10.2 Selection Procedures
    3. 10.3 The Subset Selection Procedure for Expectations
    4. 10.4 Optimal Combination of the Indifference Zone and the Subset Selection Procedure
    5. 10.5 Selection of the Normal Distribution with the Smallest Variance
    6. 10.6 Multiple Comparisons
    7. References
  13. 11 Generalised Linear Models
    1. 11.1 Introduction
    2. 11.2 Exponential Families of Distributions
    3. 11.3 Generalised Linear Models – An Overview
    4. 11.4 Analysis – Fitting a GLM – The Linear Case
    5. 11.5 Binary Logistic Regression
    6. 11.6 Poisson Regression
    7. 11.7 The Gamma Regression
    8. 11.8 GLM for Gamma Regression
    9. 11.9 GLM for the Multinomial Distribution
    10. References
  14. 12 Spatial Statistics
    1. 12.1 Introduction
    2. 12.2 Geostatistics
    3. 12.3 Special Problems and Outlook
    4. References
  15. Appendix A: List of Problems
  16. Appendix B: Symbolism
  17. Appendix C: Abbreviations
  18. Appendix D: Probability and Density Functions
  19. Index
  20. End User License Agreement

Product information

  • Title: Applied Statistics
  • Author(s): Dieter Rasch, Rob Verdooren, Jürgen Pilz
  • Release date: October 2019
  • Publisher(s): Wiley
  • ISBN: 9781119551522