Applied Statistics

DIETER RASCH, PHD, is scientific advisor at the Center for Design of Experiments at the University of Natural Resources and Life Sciences, Vienna, Austria. He is also an elected member of the International Statistical Institute (ISI) and the Institute of Mathematical Statistics (IMS).ROB VERDOOREN, PHD, is a Consultant Statistician at Danone Nutricia Research, Utrecht, The Netherlands.JÜRGEN PILZ, PHD, is the Head of the Department of Applied Statistics at AAU Klagenfurt, Austria. He is also an elected member of the International Statistical Institute (ISI) and the Institute of Mathematical Statistics (IMS).

• Preface xi1 The R-Package, Sampling Procedures, and Random Variables 11.1 Introduction 11.2 The Statistical Software Package R 11.3 Sampling Procedures and Random Variables 42 Point Estimation 112.1 Introduction 112.2 Estimating Location Parameters 122.3 Estimating Scale Parameters 242.4 Estimating Higher Moments 272.5 Contingency Tables 293 Testing Hypotheses – One- and Two-Sample Problems 393.1 Introduction 393.2 The One-Sample Problem 413.3 The Two-Sample Problem 634 Confidence Estimations – One- and Two-Sample Problems 834.1 Introduction 834.2 The One-Sample Case 844.3 The Two-Sample Case 965 Analysis of Variance (ANOVA) – Fixed Effects Models 1055.1 Introduction 1055.2 Planning the Size of an Experiment 1065.3 One-Way Analysis of Variance 1085.4 Two-Way Analysis of Variance 1155.5 Three-Way Classification 1346 Analysis of Variance –Models with Random Effects 1596.1 Introduction 1596.2 One-Way Classification 1596.3 Two-Way Classification 1766.4 Three-Way Classification 1867 Analysis of Variance –Mixed Models 2017.1 Introduction 2017.2 Two-Way Classification 2017.3 Three-Way Layout 2238 Regression Analysis 2578.1 Introduction 2578.2 Regression with Non-Random Regressors – Model I of Regression 2628.3 Models with Random Regressors 3229 Analysis of Covariance (ANCOVA) 3399.1 Introduction 3399.2 Completely Randomised Design with Covariate 3409.3 Randomised Complete Block Design with Covariate 3589.4 Concluding Remarks 36510 Multiple Decision Problems 36710.1 Introduction 36710.2 Selection Procedures 36710.3 The Subset Selection Procedure for Expectations 37110.4 Optimal Combination of the Indifference Zone and the Subset Selection Procedure 37210.5 Selection of the Normal Distribution with the Smallest Variance 37510.6 Multiple Comparisons 37511 Generalised Linear Models 39311.1 Introduction 39311.2 Exponential Families of Distributions 39411.3 Generalised Linear Models – An Overview 39611.4 Analysis – Fitting a GLM – The Linear Case 39811.5 Binary Logistic Regression 39911.6 Poisson Regression 41111.7 The Gamma Regression 41711.8 GLM for Gamma Regression 41811.9 GLM for the Multinomial Distribution 42512 Spatial Statistics 42912.1 Introduction 42912.2 Geostatistics 43112.3 Special Problems and Outlook 450References 451Appendix A List of Problems 455Appendix B Symbolism 483Appendix C Abbreviations 485Appendix D Probability and Density Functions 487Index 489