A well-done book on statistics that includes all the theorems I can think of. Goes into situations and proofs that require Calculus, but that isn't really a big deal.

The book is pretty standard, though I don't know how good MINITAB is since I have neither used nor heard of it. Apparently, it is a statistics package. Then again, I suppose I could modify it for SciPy or something along those lines.

In any case, this book is really good at explaining things. My only problem is that my grounding in Calculus is so tenuous that I don't really have a grasp of the proofs presented. All I can do is smile and nod at the results. So I probably need to go back to Calculus and try to learn that on my own. ( )

Strengths:
* Historical background is very good
* "Second look at Statistics" articles are very informative
* MATLAB appendices introduce computation after the theory.
* Abundant case studies that demonstrate breadth of applications
* Good introduction to combinatorics with interesting problems

Weaknesses:
* Does not bring linear algebra ideas into the text whatsoever. This is a very important viewpoint, especially for regression and ANOVA
* The text feels scattered at times with too many diversions. Readability and flow feels off.
* Half the text is on probability and the other half on statistics. This makes sense, but is too disjointed. The statistical ideas come out very late and abruptly.
* MATLAB is expensive -- R and Python are the current go-to languages for computation and exploration. Needs more emphasis on this.

Overall, I appreciate this book more now after having more time to understand both probability and statistics and it serves as a good reference. However, as an intro book, it tries to present too many ideas at once and needs a lot of additional planning and guidance. ( )