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Applied Statistics: A Procedure Solution with R





Applied Statistics: A Procedure Solution with R

Dieter Rasch

ISBN: 978-1-119-55152-2 October 2019 472 Pages


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. 


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

12 Spatial Statistics