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