Practical Linear Regression with Matlab or R I am going to teach undergraduate laboratory exercises for linear regression models in either Matlab or R.
Is there any book that is not emphasizing a lot in theory but focuses mostly in applications and in examples with any of the above languages?
I want to thank you all for your insights and for time replying to me. 
I cannot accept an answer, this wouldn't be fair for the others since everyone contributed.
 A: Peter Dalgaard, one of the R core contributors, has written Introductory Statistics with R. I don't know what your class is and what backgrounds your students will have, but you may want to glance a (brief) table of contents on the book's Web site. There are several chapters on linear regression.
A: Please take a look at Applied Predictive Modeling Kuhn and Johnson
http://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/dp/1461468485
They have an R Package with all the data and nicely available,actual code. There are at least two case studies in the book involving simple least squares with plots and analysis. There are exercises in the computing sections and the end of each chapter and real world data.  They also have a blog and website for technical support (says to contact them if you want to use their book for class.)  Probably could strike a deal as the book isn't cheap. But it is really good. These guys are the authors of the caret package, in R.
http://appliedpredictivemodeling.com/blog/
I think the book is outstanding--emphasis is on applied if you read the Amazon reviews and I can vouch for that!
A: You may also want to look at The R Book by Crawley (http://www.amazon.com/The-Book-Michael-J-Crawley/dp/0470973927), which I find to be a great toolbox-type resource for R modeling. It runs through high-level implementations of many common modeling techniques using simple datasets, and will frequently reuse the same datasets to show how different models compare.
It is not completely theory-free and probably a little long if your topic is just one part of a larger class, but the points at which he applies theory are usually used to show how to derive the answer in R outside of the modeling functions, which I found useful.
