Comprehensiveness rating: 5 read less
This is a tutorial that covers basic areas and ideas of linear regression. It covers this material through carefully selected examples. R, the software used to present examples in the text, is an open source software which is appropriate and convenient for an open textbook. The book provides an effective and complete index and table of content with page numbers as links to the text.
Accuracy rating: 5
The open source software (R) used to present data is as accurate as any commercially available software. The rest of the content is accurate and error-free.
Relevance/Longevity rating: 5
As in introductory text, the content is up-to-date. As a basic topic in regression theory, linear regression is here to stay. With the current growth of data mining it is difficult to imagine the future of data analytics without linear regression. The text is written and arranged in such a way that important updates will be easy to implement.
Clarity rating: 5
The text is clear and accessible to readers with standard elementary statistical background. It provides explicit guidance for R and the context for statistical terms is clear. The concepts are well explained.
Consistency rating: 5
The exposition is consistently clear and well-motivated by examples. The level and presentation is consistent as well. The text uses consistent, standard, and elementary terminology appropriately introduced to deal with linear regression models.
Modularity rating: 5
The text, not overly self-referential, is presented in eight chapters, each with a hyperlink to the text. Each chapter has short sections. In addition, each page number in the Index is a hyperlink to the text.
Organization/Structure/Flow rating: 5
The topics in the text are well motivated by examples that should make the subject more interesting to the reader. The organization is excellent, making each topic clear and easy to read.
Interface rating: 4
It would have been nice to have color images in the Figures. Also, Figure 4.1 (CHAPTER 4. MULTI-FACTOR REGRESSION) would be clearer if it showed only a few of the pairwise comparisons for the Int2000 data frame. But these are just two minor issues of display.
Grammatical Errors rating: 5
I did not find grammatical errors.
Cultural Relevance rating: 5
The text is not culturally insensitive or offensive in any way. It uses examples that are culturally neutral.
I would use this tutorial in any undergraduate course dealing with linear regression.