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Applied Statistics
Theory and Problem Solutions with R
Dieter Rasch (Author), Rob Verdooren (Author), Jürgen Pilz (Author)
9781119551522, Wiley
Hardback, published 11 October 2019
512 pages
24.1 x 17.8 x 3.1 cm, 0.862 kg
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. 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.
Preface xi 1 The R-Package, Sampling Procedures, and Random Variables 1 2 Point Estimation 11 3 Testing Hypotheses – One- and Two-Sample Problems 39 4 Confidence Estimations – One- and Two-Sample Problems 83 5 Analysis of Variance (ANOVA) – Fixed Effects Models 105 6 Analysis of Variance –Models with Random Effects 159 7 Analysis of Variance –Mixed Models 201 8 Regression Analysis 257 9 Analysis of Covariance (ANCOVA) 339 10 Multiple Decision Problems 367 11 Generalised Linear Models 393 12 Spatial Statistics 429 References 451
1.1 Introduction 1
1.2 The Statistical Software Package R 1
1.3 Sampling Procedures and Random Variables 4
2.1 Introduction 11
2.2 Estimating Location Parameters 12
2.3 Estimating Scale Parameters 24
2.4 Estimating Higher Moments 27
2.5 Contingency Tables 29
3.1 Introduction 39
3.2 The One-Sample Problem 41
3.3 The Two-Sample Problem 63
4.1 Introduction 83
4.2 The One-Sample Case 84
4.3 The Two-Sample Case 96
5.1 Introduction 105
5.2 Planning the Size of an Experiment 106
5.3 One-Way Analysis of Variance 108
5.4 Two-Way Analysis of Variance 115
5.5 Three-Way Classification 134
6.1 Introduction 159
6.2 One-Way Classification 159
6.3 Two-Way Classification 176
6.4 Three-Way Classification 186
7.1 Introduction 201
7.2 Two-Way Classification 201
7.3 Three-Way Layout 223
8.1 Introduction 257
8.2 Regression with Non-Random Regressors – Model I of Regression 262
8.3 Models with Random Regressors 322
9.1 Introduction 339
9.2 Completely Randomised Design with Covariate 340
9.3 Randomised Complete Block Design with Covariate 358
9.4 Concluding Remarks 365
10.1 Introduction 367
10.2 Selection Procedures 367
10.3 The Subset Selection Procedure for Expectations 371
10.4 Optimal Combination of the Indifference Zone and the Subset Selection Procedure 372
10.5 Selection of the Normal Distribution with the Smallest Variance 375
10.6 Multiple Comparisons 375
11.1 Introduction 393
11.2 Exponential Families of Distributions 394
11.3 Generalised Linear Models – An Overview 396
11.4 Analysis – Fitting a GLM – The Linear Case 398
11.5 Binary Logistic Regression 399
11.6 Poisson Regression 411
11.7 The Gamma Regression 417
11.8 GLM for Gamma Regression 418
11.9 GLM for the Multinomial Distribution 425
12.1 Introduction 429
12.2 Geostatistics 431
12.3 Special Problems and Outlook 450
Appendix A List of Problems 455
Appendix B Symbolism 483
Appendix C Abbreviations 485
Appendix D Probability and Density Functions 487
Index 489
Subject Areas: Mathematics [PB]
