Buch, Englisch, 512 Seiten, Format (B × H): 180 mm x 252 mm, Gewicht: 908 g
Theory and Problem Solutions with R
Buch, Englisch, 512 Seiten, Format (B × H): 180 mm x 252 mm, Gewicht: 908 g
ISBN: 978-1-119-55152-2
Verlag: Wiley
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.
Autoren/Hrsg.
Weitere Infos & Material
Preface xi
1 The R-Package, Sampling Procedures, and Random Variables 1
1.1 Introduction 1
1.2 The Statistical Software Package R 1
1.3 Sampling Procedures and Random Variables 4
References 10
2 Point Estimation 11
2.1 Introduction 11
2.2 Estimating Location Parameters 12
2.2.1 Maximum Likelihood Estimation of Location Parameters 17
2.2.2 Estimating Expectations from Censored Samples and Truncated Distributions 20
2.2.3 Estimating Location Parameters of Finite Populations 23
2.3 Estimating Scale Parameters 24
2.4 Estimating Higher Moments 27
2.5 Contingency Tables 29
2.5.1 Models of Two-Dimensional Contingency Tables 29
2.5.1.1 Model I 29
2.5.1.2 Model II 29
2.5.1.3 Model III 30
2.5.2 Association Coefficients for 2 ×2 Tables 30
References 38
3 Testing Hypotheses – One- and Two-Sample Problems 39
3.1 Introduction 39
3.2 The One-Sample Problem 41
3.2.1 Tests on an Expectation 41
3.2.1.1 Testing the Hypothesis on the Expectation of a Normal Distribution with Known Variance 41
3.2.1.2 Testing the Hypothesis on the Expectation of a Normal Distribution with Unknown Variance 47
3.2.2 Test on the Median 51
3.2.3 Test on the Variance of a Normal Distribution 54
3.2.4 Test on a Probability 56
3.2.5 Paired Comparisons 57
3.2.6 Sequential Tests 59
3.3 The Two-Sample Problem 63
3.3.1 Tests on Two Expectations 63
3.3.1.1 The Two-Sample t-Test 63
3.3.1.2 The Welch Test 66
3.3.1.3 The Wilcoxon Rank Sum Test 70
3.3.1.4 Definition of Robustness and Results of Comparing Tests by Simulation 72
3.3.1.5 Sequential Two-Sample Tests 74
3.3.2 Test on Two Medians 76
3.3.2.1 Rationale 77
3.3.3 Test on Two Probabilities 78
3.3.4 Tests on Two Variances 79
References 81
4 Confidence Es
