Any edition of this will be useful for you, and the older editions are less expensive.

I can find the Table of Contents of the 7th edition online at Amazon.
http://www.amazon.com/s/ref= nb_sb_noss?url=search-alias% 3Dstripbooks&field-keywords= wackerly+mathematical+ statistics+with+applications+ 5th+edition&x=0&y=0

I have the 5th edition, and it has the same Table of Contents except that the 16th chapter of the 7th edition is not present in the 5th edition.

So look at that Table of Contents and compare it to the edition you have as you read my comments here.

Chapter 1.   A very quick overview of statistics.   I presume you know most of this from your statistics courses.

Chapter 2.  We will not cover most of this in our course.  I will assign problems on the Bayes Rule, section 2.10.   That requires an understanding of conditional probabilties, which, of course, requires an understanding of the laws of probability.    So see how much of Ch. 2 you need in order to understand section 2.10.    I will not really teach any of this, including Bayes Rule, but we will do a Bayes Rule problem in class.   I'm assuming I'm just reminding students of what they already know.

Chapters 3-4:   Wackerly, et. al. introduce the basic ideas about distributions here and illustrate them in many examples of distributions.  These basic ideas include pdfs, pmfs, cdfs, moment-generating functions, expected values, variances, etc.     In Casella/Berger (called CB for the rest of this note) an overview of this is given in Ch. 1 and parts of Ch. 2.   We will talk in class, and work problems on, those ideas on about Days 3 and 4 of the class.   I will assume these are not new ideas to you, and we are mainly thinking about the "fine points" of understanding them fully.

As for the names of dist'ns and facts about them, that is covered in a couple of sections of CB Ch. 3.   You are not expected to memorize any of these, but will have notes available to you on the test.  (Probably you will wind up memorizing some of it, just because you use it often.  For instance, most of the facts about the normal and binomial distributions and perhaps  a few others.)    As we discuss these in class, and cover Chapter 3, and you do the homework, you will increase your skill in making the plan about how to derive ANY result, and increase your skill in using various calculus techniques to carry them out.    Sometimes in homework and on tests, you are expected to derive some result that is in the notes, which includes the table at the back of CB.   On any particular problem, if you are uncertain about whether to derive it or just use the result, ask. 

Chapter 6:  This is very important material.   In our course, we'll start this material at the end of Chapter 1 of CB, and it is an important part of Chapter 2 of CB - perhaps the hardest part of Chapter 2.   It is particularly important - all statistics are functions of random variables, and in theoretical statistics, we want to be able to derive the sampling distributions of various statistics.    In order to deal with this, you have to have a clear understanding of, and be good at using, cumulative distn functions and prob. density functions/prob. mass functions.   Those were introduced in sections 3.2 and 4.2.   We will do a brief review of that at the end of Ch. 1.   That's on the second day of class, before HW 1 is due.

Chapter 5.    This is background material for Chapter 4 of CB.   

Chapter 7.   This is background for Chapter 5 of CB.  

So that's a review of the probability.

Chapters 8-10.   These cover material in statistics.    We will talk about much of this VERY BRIEFLY during the review of prerequisites part of the first semester.   Everything we talk about in the first semester is presumed to be review.   (MLEs, Bias of an estimator, computing the variance of an estimator, doing a hypothesis test, computing the power of a hypothesis test.)    The material in our CB on these topics is in the second semester in Chapters 7-10 of CB.   It is much more theoretical than the coverage in Wackerly.   So having the examples in Wackerly to look at will be very useful. 

Our graduate course in Math Stat does not cover Regression or Analysis of Variance because those are covered in the other Core courses of the master's program.    This course does cover the basic theory of Bayesian statistics.  I do not assume students already know that and have an introductory handout on it.    This is covered around the end of the first semester or the beginning of the second semester.  In CB it first arises in Chapters 6-7.  One of the important probability techniques needed to do theoretical Bayesian analysis is covered in CB Chapter 4 - Hierarchical models.