Textbooks for Undergraduate Statistics Major

[Bayesian1701]

My undergraduate major is  Mathematics and a minor in Economics, and my college offers three real stats classes (Mathematical Stat 1/2, Stats for Engineers/Scientists (applied calculus based stats).  I know that Statistics undergraduate programs aren't that common (at least compared to math), and that I don't need a stats major/minor to get into grad school for stats.   I would like to self-study a little more stats before I (hopefully)  start atStats grad program in Fall 2018.  For those of you with an undergraduate minor/major what classes did you take and what textbooks did you use?  I have Wackerly Math Stat already.  I am looking  for textbook suggestions that are more undergraduate focused and do not require anything above Real Analysis I. 

I would like a Regressions book and a book on Bayesian Statistics for sure, and anything else that you think would be helpful.

[abstract_art]

The standard undergrad math stats books are Wackerly, Rice, and Larsen and Marx. You only really need to self study from one. If you want to preview what you'd most likely see in grad school, Casella and Berger is the standard for the first year of a grad program.  For Bayesian stats the standard is Bayesian Data Analysis (BDA) (Hoff also seems like another pretty standard bayesian stats book). None of these require real analysis (but it'd help to have some analysis).

[GoPackGo89]

What is covered in your college's math/stat sequence? You will obviously see much more in grad school but maybe a little self study on linear models? The department I'm in has this as a requirement for both a minor and major in stats. A quick stack exchange search of "Linear models textbooks" nets some decent threads with recommendations.

[anon333]

Honing in on your theoretical knowledge of linear algebra, real analysis (proofs of calculus concepts, heavily recommended), and even probabilistic/measure theory (not required) will be more than enough preparation for Statistics Graduate school. Also learning common statistical coding languages such as R and Python are useful, as well as some comp sci classes (objected-oriented programming, data structures, algorithms are what I recommend). Although called a "Statistics" PhD, you won't need too much Statistical knowledge (from undergrad) to succeed in it.

 

Also order of importance when it comes time to apply:

LoR's (Letter of Recs), GPA, GRE, GRE subject

 

My biggest regrets are:

-not having more, advanced theoretical knowledge in math

-not taking GRE subject

 

 

Actual answer to question:

Elements to Statistical Learning,

Hoff--First Course in Bayesian Statistical Methods,

Introduction to Statistical Learning with Applications in R (easiest read out of them all)

Edited March 27, 2017 by anon333

[abstract_art]

I don't know if I'd suggest Elements to someone who's just trying to self study some stats before a graduate program, especially if they weren't specifically interested in machine learning

[anon333]

My mistake! You are right, that book (and Intro to Stats Learning) is definitely geared more towards machine learning. 

Edited March 27, 2017 by anon333