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 nonstandard problems confidently. Taking a practical approach to applied statistics, this userfriendly 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 preexperimental as well as postexperimental 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 Rprograms 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
 Cover
 Preface
 1 The R‐Package, Sampling Procedures, and Random Variables
 2 Point Estimation
 3 Testing Hypotheses – One‐ and Two‐Sample Problems
 4 Confidence Estimations – One‐ and Two‐Sample Problems
 5 Analysis of Variance (ANOVA) – Fixed Effects Models
 6 Analysis of Variance – Models with Random Effects
 7 Analysis of Variance – Mixed Models
 8 Regression Analysis
 9 Analysis of Covariance (ANCOVA)
 10 Multiple Decision Problems

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
 Appendix A: List of Problems
 Appendix B: Symbolism
 Appendix C: Abbreviations
 Appendix D: Probability and Density Functions
 Index
 End User License Agreement
Product information
 Title: Applied Statistics
 Author(s):
 Release date: October 2019
 Publisher(s): Wiley
 ISBN: 9781119551522
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