Linear Regression Using R: An Introduction to Data Modeling

Reviewed by Mamunur Rashid, Associate Professor, DePauw University on 5/19/23

Comprehensiveness rating: 4 see less

Yes, the text covers most of the areas. This is a comprehensive and accessible guide that adeptly combines the principles of linear regression with practical implementation using the R programming language.

Content Accuracy rating: 5

Yes, the accurate, error-free, and unbiased. For the data tables, authors could create actual tables for better understanding.

Relevance/Longevity rating: 4

The content is up-to-date, and it is easy to understand for audiences with non-math backgrounds. The authors have struck a balance in presenting the material in a way that is not overly dependent on specific software versions or rapidly changing technologies. This approach helps to mitigate the risk of the text quickly becoming obsolete as the author used the base package of R.

Clarity rating: 4

The notable strengths of this book are its readability and the ease with which readers can follow along. The authors, David J. Lilja and Greta M. Linse have done an excellent job of presenting complex concepts in a clear and concise manner, making the book accessible to readers with varying levels of expertise.

Consistency rating: 4

It is commendable that the text maintains a quite appropriate level of internal consistency in its terminology and framework. The authors have taken care to establish and adhere to a consistent set of terms and concepts throughout the book. This consistency helps readers develop a clear understanding of the subject matter without confusion or ambiguity and readers are able to follow along more easily and make connections between different sections of the book.

Modularity rating: 4

The text's organization, use of subheadings, and clear section breaks enable easy division into smaller reading sections, making it adaptable for different teaching approaches and facilitating targeted learning. The authors' efforts to balance context and self-containment contribute to the text's modularity, allowing for smooth reorganization and realignment of content without causing significant disruption to the reader. However, I recommend adding a chapter with two independent variables.

Organization/Structure/Flow rating: 4

The authors have structured the book in a coherent manner that guides readers through the content in a logical sequence. The chapters and sections are arranged in a progressive order, building upon previously introduced concepts and allowing readers to develop a solid foundation in linear regression modeling.

Interface rating: 5

The book is perfect with respect to the interface.

Grammatical Errors rating: 5

I find that the authors maintain grammatical accuracy in the book, which enhances its readability, comprehension, and establishes credibility

Cultural Relevance rating: 5

None

Comments

I commend the authors for writing this book in a manner that does not rely on mathematics, making it accessible to a wide range of readers. The approach taken allows individuals from various backgrounds to engage with the material and grasp the concepts without feeling overwhelmed by complex mathematical equations. Personally, I plan to incorporate some of the topics covered in this book into one of my classes.

Reviewed by Jiaying Liu, Assistant Professor, University of Southern Indiana on 5/12/22

Comprehensiveness rating: 3 see less

The textbook is thorough enough for students learning regression using R for the first time, which is exactly what it is designed to be. There is no mathematics or statistics involved in explaining regression. Self-learners and students will appreciate the photographs along the route. It is not recommended for in-depth learners.

Content Accuracy rating: 4

The book's accuracy is good. Again, it is not ideal for in-depth learners.

Relevance/Longevity rating: 3

It is quite useful since R is extremely multi-disciplinary, and the material of this textbook covers a wide range of application areas with examples. This is a good material for one chapter or module of a semester long course.

Clarity rating: 4

The visuals and transitions are clearly explained in the supporting text, making everything very apparent. 

Consistency rating: 5

This book's straightforward and concise wording makes it very reader-friendly. Identical dataset and variables are used throughout the whole book.

Modularity rating: 5

Sections are easy to follow and provide for many beginning and stopping positions.

Organization/Structure/Flow rating: 5

The content follows a logical and straightforward course, with relevant information gathered together.

Interface rating: 4

Not a whole issues here. Some codes are break apart to the next pages.

Grammatical Errors rating: 5

No issues here.

Cultural Relevance rating: 5

There was nothing in the textbook that seemed culturally insensitive or offensive.

Comments

It is a great book for first learners who like to get their feet wet in the regression modeling using R. But certainly need more in-depth material to supplement this.

Reviewed by Fariba Khoshnasib, Visiting assistant professor, Earlham College on 3/2/21

Comprehensiveness rating: 3 see less

I was thinking of using this for a course in linear regression but unfortunately this doesn't include any of the mathematics behind the analysis. This can be used simply to learn how to do regression in R in a few hours without any knowledge of the algorithm used or the mathematics that goes into it.

Content Accuracy rating: 5

The material and the CSV files provided for practicing regression with data is accurate and useful. The pictures included in the body of the text are very helpful to new R learners.

Relevance/Longevity rating: 4

It could be more comprehensive as a textbook in linear regression. But it simply doesn't work as a college level textbook. Perhaps just used for one week of the course.

Clarity rating: 5

The writing and the pictures and the data attached are very accessible and clear. It's amazing that the content is hyperlinked,too.

Consistency rating: 5

The material is consistently taught at the same level of difficulty which makes is accessible for high school or middle school students.

Modularity rating: 5

The way the content is organized makes it easy to read and follow the topics with a good flow.

Organization/Structure/Flow rating: 5

The order of the material presented in the book is logical and accessible for an easy level of difficulty.

Interface rating: 5

The images and hyperlinked content are string areas of the book that attracted me to browse through.

Grammatical Errors rating: 5

I didn't notice any grammatical errors.

Cultural Relevance rating: 5

Nothing insensitive in the data examples or content.

Comments

More appropriate for elementary audience of linear regression. Not appropriate for a data science program. I would recommend this for middle school or high school students.

Reviewed by Mark Greenwood, Professor, Montana State University - Bozeman on 12/29/20

Comprehensiveness rating: 3 see less

It is very focused and short treatment of simple and multiple linear regression. It does not cover categorical predictors, interactions of predictors, doesn't spend much time interpreting slope coefficients, or discuss confidence intervals for them, and has some technical issues with the methods discussed. So it is not a very comprehensive treatment of the topic but does provide a short introduction to simple and multiple linear regression model building with quantitative predictors and how to use R to do that. I like Chapter 5 best and the diagram for training and validation and how this fits into an engineering perspective on statistical model building and evaluation.

Content Accuracy rating: 1

There are issues of bias not discussed that could be discussed with the missing data discussion and the sloppiness of this discussion propagates into issues with models and comparing models for different sets of responses that are problematic. Some of the notation is a little bit loose for the statistical modeling and inferences discussed – for example there is no distinction between population and sample estimates in the models. The example used is interesting to the potential engineering audience but there is little discussion of sampling of subjects/measurements and implication of that – especially as applied to the data set. There are some incorrect interpretations of p-values and the overall F-test. And some modeling choices are difficult to understand (including both square-root and original versions of the same predictors – why do we need both? How would you interpret the model with both? Doesn't this create multicollinearity issues?).

Relevance/Longevity rating: 4

It presents a good introduction to the topics promised. I am not sure this would be enough for a full semester of a course – more of a unit for a course. And it is a bit sparse on tools to prepare students to try to use these methods on their own similar data sets. It also may get dated with changes to the statistical software used within it – although the code used should last because it just uses “base R” functions. This also means it doesn’t leverage any evolutions that are occurring in statistical computing

Clarity rating: 4

It is well written. Some of the technical issues cause me concern in using it for a course, but it is a nice book to read and has some interesting points covered.

Consistency rating: 5

It is internally consistent with how it presents the material.

Modularity rating: 3

It is not very modular and I would use all of it together as it builds on earlier material.

Organization/Structure/Flow rating: 5

The organization of the topics is reasonable.

Interface rating: 4

The links appear to be dynamic within the document but do not seem to work on my system. It is otherwise a nice platform and formatting for the material.

Grammatical Errors rating: 4

I am a bit pedantic on this, but “data” in statistics is a plural word and “datum” is singular. Some other wording issues would be characterized/defined differently if written by a statistician. These issues would prevent my use of the book in my classes. The word “significant” is used in multiple ways without clarification that it can be used to mean very different things.

Cultural Relevance rating: 5

The one example data set relates to computers and there is nothing else that might either possibly allow incorporation of, or cause concerns about, different cultural perspectives.

Comments

There are some technical issues with the discussions and examples that a discussion with a statistician could aid in resolving. I think it could resonate with an engineering audience and with some modest changes be very successful at presenting the material to that audience. I really liked Chapter 5 and found that to be the most successful topic presented.

Reviewed by James Squires, Assistant Professor of Economics, Franklin College on 12/19/18

Comprehensiveness rating: 5 see less

For a introduction/tutorial to linear regressions with R, this book quickly guides a novice to building a linear model and testing it.

Content Accuracy rating: 5

My only problem is, the author calls variables in data sets "parameters". Within the context of linear regressions, I believe the term "parameters" should be reserved for coefficients in the model that will be estimated.

Relevance/Longevity rating: 5

By showing linear regressions with the statistical software R, the book gives a modern and hands on approach the material.

Clarity rating: 5

I think the best thing about this book is it's clarity. The clear and concise language of this book makes it very friendly to readers.

Consistency rating: 5

By using one variable that is modeled throughout the entire book, it allows for a nice connectiveness between chapters.

Modularity rating: 5

The book is nicely broken down into easily digestible parts.

Organization/Structure/Flow rating: 5

Topics appropriately build on each another.

Interface rating: 5

I experienced no interface issues.

Grammatical Errors rating: 5

I detected no grammar issues.

Cultural Relevance rating: 5

The book is free of any cultural sensitive topics.

Comments

For the potential reader with little R programming and data science background, this book quickly allows someone to build a linear model from a given data set. Also, the book has a nice introduction to training and testing a linear model.
With the authors clear and easy to read explanations, this will be a text that I will refer to people to for quickly running linear regressions in R.

Reviewed by Robert Leonard, Assistant Professor, Miami University on 2/1/18

Comprehensiveness rating: 3 see less

There are basic functions such as class() or typeof() that should be introduced early on for any user of R. Also, A practical explanation of residual standard error or what a nonsensical model for the example used throughout the text would be helpful for a beginner.

Content Accuracy rating: 3

Using vocabulary to help student differentiate between an assumed model and a prediction equation would be helpful if you are planning to use this as a classroom text. Depending on how you are used to teaching regression, you may find many problematic uses of vocabulary or you may find none.

Relevance/Longevity rating: 5

This text could easily be updated by either replacing parts or by adding new material.

Clarity rating: 4

The main vocab is touched on and explained well, minus some possible misuse of terminology depending upon how one teaches regression. As for technical R vocab, the use of 'row' early on in the text to describe the header of a data frame could also be problematic since the first row of a data frame typically refers to the first row of data, not the names of the columns.

Consistency rating: 5

Delivery and organization is consistent throughout.

Modularity rating: 5

Though additional material is needed in between, what is presented is nicely laid out.

Organization/Structure/Flow rating: 5

The text follows the typical presentation of a traditional look at regression, which makes for a text that is clear and well organized.

Interface rating: 4

There are places where code chunks unnecessarily spill over to the next page and some figures/tables that need to be relocated so that the reader does not come to them before they have actually been referred to in the text.

Grammatical Errors rating: 5

No issues

Cultural Relevance rating: 5

I think all is fine. I wouldn't see any particular computer processor feeling like they have been misrepresented or purposely left out.

Comments

I hope this review actually goes through this time??? It is my third attempt at trying to complete this before Qualtrics times me out. Sorry that I am so slow.......

Also, below is a paragraph style review. I wrote this before seeing the actual format was going to be a survey type of setup.

Though well written and organized, this book may not be your "go to" resource if you're looking for a textbook or supplementary material when teaching an intro R and/or regression course. The author does use an example throughout that many can understand at least to some degree (influencers of computer performance), which exposes the reader to the useful concept that knowledge about a data set can be extremely useful. Further, the introduction of functions like attach() and update() are examples of how the author has nicely woven into the content a practical approach to how coding is part of analysis. The exploratory use of plot() to visualize the data before introducing a one-factor regression is another positive example of this. However, there are some places throughout the book that might make you seriously question whether you could teach a course using this book (either as a stand-alone resource or just a supplementary one). The wording in some places can be confusing or even contradictory depending upon how you present regression, especially as an introduction to the topic where consistent use of vocabulary can be crucial. For example, consider in Section 3.2 where the mathematical form in (3.1) is referred to as the 'simplest regression model' yet the regression equation in (3.2) is similarly referred to as the 'final regression model'. Personally, I try to differentiate these two things for students first learning these concepts by stressing 'assumed model' using y as response and 'prediction equation' using y_hat as the response. Maybe this example isn't problematic for you, nonetheless I still suggest you carefully look through the entire book before adopting it as a resource for your students.

Reviewed by Jairo Santanilla, Professor, University of New Orleans on 2/8/17

Comprehensiveness rating: 5 see 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.

Content 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.

Comments

I would use this tutorial in any undergraduate course dealing with linear regression.