Conditions of Use
The text covers material for a one-semester long class on applied statistics. I teach a course on biological statistics and it covers most of the areas that I teach in my course. Some topics I spend some time on are slightly covered – ANCOVA,... read more
The text covers material for a one-semester long class on applied statistics. I teach a course on biological statistics and it covers most of the areas that I teach in my course. Some topics I spend some time on are slightly covered – ANCOVA, randomized blocks, mixed models. Some material on logistic regression might be useful. However, I can easily add more material using my notes and examples. I found the material to be current, accurate and free of errors. There is also a nice set of case students for the students to look at to get a sense of what real data is like. I tend to add two things to my course - first I make a number of "how do I" examples with screenshots to help students navigate R and Rstudio. Second I have students read or try to reproduce analyses in publications so they can get a sense of "good" and "bad" analyses. The case studies at the end of the text more or less do this as well so I do not see this as a problem with the text.
The writing is good quality and I did not find any errors or coding problems. I like the summary at the end of each chapter about highlights and coding.
The book is up to date and the material is quite relevant and current. It will have to be updated regularly as it is based on R (which changes regularly). The inclusion of tidyverse is both good and bad in that some students struggle with this topic.
The authors have done a good job at explaining statistical analysis and the tools that are available to evaluate data. It would be nice for the authors to add some comments in the coding to help students recall what is being done although again this something easy for me to add. Comments might, for example, help explain some of the ggplot coding. I have found that coding in R is the greatest issue in my course so have numerous examples for them to follow.
The text is consistent in terminology and the chapters have a similar structure. Again, I like the summaries at the end of each chapter.
The text is free of significant interface issues, including navigation problems, distortion of images/charts, and any other display features that may distract or confuse the reader. It is quite easy to find topics and link to specific chapters. There might be some minor issues moving chapters around as the examples use code and some of the explanations are in other chapters. This is a rather minor problem though.
I have a slightly different ordering of topics in my course however, it would not be a problem to teach most of the chapters in a different order although there are some obvious chapters that you want to teach in sequence (one-way ANOVA before two-way ANOVA or SLR before MLR). I tend to teach simple regression and correlation before ANOVA so that I can better explain the estimates that are produced in the ANOVA summary. Again, this is not a problem as the SLR chapter is relatively independent of the other chapters and can be taught earlier. Since the general structure of the text is quite sound, I can easily supplement the text with additional examples and material. The case studies section is especially useful.
The authors used bookdown to prepare the text. I believe (once the first edition is done) this makes it easy to update, produces a consistent set of graphics and helps with navigation. To update should also be relatively easy.
The book has been through a number of editions so the grammar/spelling etc is good.
I did not find any issues.
I look forward to using this as the main text the next time I teach my class. It is a better text than many of the others that involve a first course in statistics using the R package.
R introduction is concise, data sets introduction is clear with objectives formulating the the questions to answer from the data. Plots for categorical variables would compliment those for quantitative variables. Grammar of graphics is condensed,... read more
R introduction is concise, data sets introduction is clear with objectives formulating the the questions to answer from the data.
Plots for categorical variables would compliment those for quantitative variables. Grammar of graphics is condensed, more would be better for an introductory textbook. Data wrangling needs to talk about the 5 verbs and also briefly talk about piping. The ASA statement on p-values would be a great addition to this textbook. Briefly introduce loops in R.
There is some missing information on page 97 on second bullet point for SS_Total.
page 154, Discussion section needs to be distinct as you comment on it earlier for reference.
The content is so up-to-date and captivating addressing interesting problems to solve using data.
There is need to be intentional in introducing the R packages and its associated jargon. Distinguish between random and fixed effects and also highlight other multiple comparison tests. For example, introduce functions such as favstats and their normal associatives for the five number summary.
The flow from chapter to chapter and within chapters is very consistent, leading to the practice problems. The structure of the practice problems is very consistent throughout the textbook.
This is a typical set-up for a Statistics textbook. However, there is need to delve deeply on modern statistics topics such as bootstrap resampling and confidence intervals.
Great organization of the topics.
the outputs from R and their associated visuals add more value to the theoretical aspects.
great communication, it is very clear.
This is a good textbook for intermediate modern statistics. Thank you for this open resource for equality in access to education.
Table of Contents
- 1 Preface
- 2 (R)e-Introduction to statistics
- 3 One-Way ANOVA
- 4 Two-Way ANOVA
- 5 Chi-square tests
- 6 Correlation and Simple Linear Regression
- 7 Simple linear regression inference
- 8 Multiple linear regression
- 9 Case studies
About the Book
Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.
About the Contributors