Statistical Tools – Te Grotenhuis and Van der Weegen (2009)

How does one teach statistics? Is it more important to start with mathematical thoroughness, or to help students to gain a conceptual understanding first? Few give a comprehensive introduction to statistics for those without the otherwise indispensable mathematical background. Manfred te Grotenhuis and Theo van der Weegen recently published an introductory book on statistics, explaining statistical concepts using words and graphs, rather than formulas.

Less than a year ago, I wrote these exact words. I then discussed the publication of a Dutch book on statistics, to which I provided minor assistance. Now, I repeat these words to introduce the Enligsh translation of this conceptual introduction to statistics, called Statistical Tools. Again, I contributed to this publication, this time by providing a first, rough, translation from Dutch to English. Let me repeat below what I wrote before on this blog, for of course this still holds relevance for the translation to English:

With the focus on practical application rather than statistical theory, the first chapter starts explaining the goal of inferential statistics, meanwhile introducing the concepts of measurement and variables. Considerable attention is paid to the importance of high quality data to perform your analyses on. The second chapter … Continue Reading

Book: A conceptual introduction to statistics

How does one teach statistics? Is it more important to start with mathematical thoroughness, or to help students to gain a conceptual understanding first? There’s something to say about both, depending on the setting you’re in, but fact is that most books on statistics (even the introductory ones) rapidly delve into the mathematical depths of inferential statistics. Few give a comprehensive introduction to statistics for those without the otherwise indispensable mathematical background. Manfred te Grotenhuis and Theo van der Weegen recently published an introductory book on statistics called “Statistics as a Tool” (Statistiek als Hulpmiddel, in Dutch), explaining statistical concepts using words and graphs, rather than formulas.

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R-Sessions 25: Book – Mixed Effects Models in S and S-PLUS (Pinheiro & Bates, 2000)

Despite the reference to S and S-PLUS in the title of this book, it offers an excellent guide for the nlme-package in R-Project. Reason for this is the close resemblance between R and S. The nlme-package, available in R-Project for estimation of both linear and non-linear multilevel models, is written and maintained by the authors of this book.
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R-Sessions 24: Book: An R and S-PLUS Companion to Applied Regression (John Fox, 2002)

For those who have some theoretical knowledge on statistics and regression techniques, and who want to learn to use R-Project to analyze some data, John Fox wrote just the book.

The introductory chapter shows the most basic aspects of R-Project. Halfway this chapter the reader finds himself analyzing real data using regression techniques. The following chapters introduce the reader to other aspects of the analytical process: reading data into your statistical program, exploring the data and performing some bivariate tests. Then, three full chapters are devoted to regression techniques. While working on practical examples, the reader is introduced to more fundamental aspects of the R-Project software where needed.
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R-Sessions 23: Book: Data Analysis Using Regression and Multilevel/Hierarchical Models — Gelman & Hill (2007)

Data Analysis Using Regression and Multilevel/Hierarchical Models

Andrew Gelman is known for his expertise on Bayesian statistics. Based on that knowledge he wrote a book in multilevel regression using R and WINbugs. This book aims to be a thorough description of (multilevel) regression techniques, implementation of these techniques in R and bugs, and a guide on interpreting the results of your analyses. Shortly put, the books excels on all three subjects.
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R-Sessions 22: Book: Introductory Statistics with R — Peter Dalgaard (2002)

Introductory Statistics with R

Peter Dalgaard is associate professor at the Department of Biostatistics at the University of Copenhagen in Denmark, and a member of the R-Project Core Development team. Also, he is an active participating and respected member of the R-help mailing-list. Based on these experiences, he set to write an introductory book on statistics and R.

The book start with relatively simple topics, easily working toward more complex statistical problems. Central techniques that are covered are analysis of variance and regression. Starting with bivariate analyses, multivariate analyses of both types are discussed to a high extent. Several types of linear (regression) models are introduced, covering polynomial regression, regression without an intercept, interactional model, two-way ANOVA with replication, and ANCOVA. A separate chapter focusses on logistic regression. Moreover, in many ways the equivalence or parallels of regression and ANOVA are discussed. Thereby, a greater understanding of the (differences between) techniques is stimulated.
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