Customer Reviews

John E. Freund's Mathematical Statistics with Applications (7th Edition)

5 star:		(4)
4 star:		(1)
3 star:		(1)
2 star:		(4)
1 star:		(8)

 

 

I have the 6th edition. I have used Probability and Statistics books from Ross, Hogg, Mendenhall, and Marx. I say this is the worst one because it has very little explanation and examples, but many unsolved exercises. Also, it is hard to follow. Here is a sentence and you be the judge:

"The exponential distribution applies not only to the occurrence of the first success in a Poisson process, which is what we call a situation like that described in Exercise 5.48, by the virtue of condition (iii) (see Exercise 6.16), it applies also to the waiting times between successes."
So, if you don't mind jumping around in the book, maybe you would not mind it as much. I am not kidding; you have to go back and forth all the time! I was just reading it, put it down, and logged in here to write this.

I like Mendenhall's "Mathematical Statistics with Applications" for quick and clear explanations (best as 1st book)
Ross' "First Course in Probability" for deeper understanding.
Hogg's "Probability and Statistical Inference" is my favorite.
Marx' Mathematical Statistics is ok (not many examples), but far better than this book.

Yes, even at the collegiate level, pictures are needed. The example problems rarely drew the diagrams/pictures. Also many of the problems refer back to previous examples in different chapters. For example, a problem in Ch 7 has us take the information for a problem in chapter 3; without a corresponding page number. Also the lay out of book is very compact. I wish the authors did not squeeze so much information on one page. The could have used more space, showed much more algebra steps, and MORE DIAGRAMS.

I'm taking a course in statistics as an independent study using this book. It might have been better had I been taking a class with a professor helping me understand the problems, but trying to understand with just this book is next to impossible. The examples skip steps, which I suppose I am just suppose to know, with no explanation or note that there have been operations omitted. This is very confusing. I have been teaching math for 34 years and feel I can follow the flow of a problem, but I teach my students to include everything to make sure their work is understood. Even the examples that are worked out sometimes have little to do with the problems that you are asked to solve. All in all, I feel this book is not well written for someone trying to learn these processes.

I am using this textbook in an undergraduate class on mathematical statistics. The book has many misspelling even at places where comprehension could be jeopardized. Many exercises tells you to refer to previous exercises which you may not have already solved. In general it is annoying that the book has too many references to previous results that are not even that important; you have to do a lot of page flipping. The textbook could have benefited from providing more examples as usually the examples after a section don't cover all that is discussed. Important results do not stand out enough for easy access and sometimes there are hidden parameters buried in the text before a theorem. There really is nothing special about this book. All problems are pretty straightforward computations. The text avoids proving how a result is derived when it is beyond the level of the text. This makes it seem like I am just plugging in formulas rather than understanding the theory. At this level, there should be better books out there. Considering how basic the book is, the price alone should dissuade you from buying it.

Yeah, I had to buy this book for an undergrad stat. class, as I'm sure most everyone did. And yeah, I agree with most of the other reviews. This is an atrocious book for learning. After the first week I threw it on the shelf and just hit the Internet. I'm really good with numbers, especially (come to find out) with statistics. I discourage you from using this book. There a book called Forgotten Statistics by Doug Downing and Jeff Clarke that you should check out. It's more of an aperitif and not nearly rigorous enough, but it does a great job of preparing you for the big time. There also a Forgotten Algebra and a Forgotten Calculus that any student should check out. I still use all three to this day for general review. Not to reference though. Not rigorous enough.

I bought this textbook for an introductory statistics course at the graduate-level. I was a bit vary of it, given the poor reviews it received here. However, I found the book to be one of the best books I've ever used.

The book is very clear on the topic and provides a complete, reasonably rigorous (for its target audience anyway) exposition on the foundations of statistics. The writing style is clear and concise, and captures the crux of each topic. The book is effectively split into two sections, basic probability theory and probability distributions/densities and basic statistical theory. You need to read the book in sequence as each chapter builds on the last. The book is self-referential, but I found it very easy to follow. This is probably because I read the book in sequential order.

There are many questions in each chapter, plenty for practice. Math is a subject only learnt by solving problems, so you will maximise what you learn from this book by attempting as many questions as possible. Solutions (final answer, not the process) to odd-numbered exercises are provided where they are not of the form "Prove ... is ...". This is good because the book does not tell you how to get to a solution (which is too tempting to read), but forces you to work through the problems. Once you find an answer, you can check what you did was correct.

I'm very surprised at the reviewers who claimed they had a mathematical background finding this book too hard. The book does NOT require much mathematical background. It does require you to be very accurate in each step of your solution as many of the equations are complicated and take 1+ pages of steps to arrive at the solution. If you make a mistake on the way (which I did quite often), it can become time-consuming to finish the questions. But this is a good thing, the book trains you to improve your accuracy.

The great thing about the book is that it focuses on the mathematical theory behind statistics. The formulae are not black boxes that you plug numbers into. You will know the limitations of statistical theory, what it actually means to have a 95% confidence interval and how you should design your tests and interpet your results. If you finish this book (and do all the questions!) you will have an excellent understanding of statistics and how to apply it in your field.

Required math to complete this book: You should be very familiar with basic algebra manipulations because the normal distribution and the gamma distribution have nasty functional forms that are difficult to work with (particularly in multivariate cases). This point should be doubly stressed as this book is completely unsuitable for someone who is not comfortable with the very basics of algebra. If you struggled with math in school, avoid this book and purchase a book that focuses on results and not the math behind the results.

You will need to know integration at a reasonable level, particularly on integrating by parts. The exponential function is used significantly in statistics so you should be comfortable with readily integrating exponential functions. Understanding matrix multiplication and how to inverse matrices is useful, but not altogether necessary , for Chapter 14 (regression).

Below is a very quick summary of the contents of the book.

Contents of the book
---------------------
1. Introduction - Covers binomial coefficients and permutations and combinations
2. Probability - Covers Sample spaces, events, basic probability theory (independent events, bayes theorem, etc.)
3. Probability distributions and densities - Covers discrete and continuous probabilities, marginal and conditional probabilities and touches upon multivariate distributions briefly
4. Mathematical expectation - Covers expected value, moment-generating functions
5. Special Probability Distributions - Covers the main probability distributions (uniform, binomial, poisson, etc.)
6. Special Probability Densities - Covers the main probability densities (normal, gamma, variations of gamma)
7. Functions of Random Variables - Covers transformation techniques
8. Sampling Distributions - Introduces the concept of statistics, discusses sampling distributions of the Mean and Variance
9. Decision Theory - We didn't cover this chapter in our course so I cannot comment on it.
10. Point Estimation - Covers methods to estimate statistics (MGF and maximum likelihood) and discusses the qualities of a good statistic (unbiased, efficient, consistent, sufficient, robust)
11. Interval Estimation - Very similar to chapter 10, but tailored to interval estimation
12. Hypothesis Testing - Introduces the basic hypothesis testing theory
13. Tests of Hypothesis involving Means, Variances and Proportions - Provides a good selection of hypothesis tests on means, differences between means, variances, proportions
14. Regression and Correlation - Provides a quick introduction into the regression analysis
15. Design and Analysis of Experiments - We didn't cover this chapter in our course so I cannot comment on it.
16. Nonparametric tests - Covers sign test, rank test

Appendices
A. Sums and Products - Provides useful formulas for dealing with sums and products
B. Special Probability Distributions - Summarizes the main probability distributions covered in the book and their main properties. Good for exam revision.
C. Special Probability Densities - Summarizes the main probability densities covered in the book and their main properties. Good for exam revision.

If NEED this book for a course, buy some supplemental books; you'll thank yourself. If you're *thinking* about buying it, stop thinking and leave this page at once.

As a [senior] Mathematics major, I've had exposure to many undergrad-level math texts. This book is among the worst. I used it in an introductory probability/stats course, and am now using it again in a second semester stats course. I consider the math department irresponsible for choosing such a horrible book.

Historically, I've been a Math nerd; in high school, I read mathworld.com for fun. In college, I've tended to prefer teaching myself the material over classroom instruction. With this book, that doesn't seem to work very well (or at least is far too time consuming).

As other people have mentioned, the book is definitely far too self-referential. Exercises, and even examples often refer back to results of previous exercises which I have not completed. The examples omit steps and explanations.(This is to an extent commonplace in math texts so the reader has to 'fill in the blanks'.) If you've already memorized and understood perfectly all the prior material in the book, that might not be an issue, but if you haven't, the examples are unclear. In a book that relies heavily on examples for instruction, the examples should be concise and detailed with reasoning provided for each step. Filling in the blanks should be left to the exercises. This book has forced me to find another book to fill the blanks in my mind. The theoretical parts of the book (proofs, etc) suffer from the same lack of quality and detail.

The book would greatly benefit from more detail and more appeal to intuition.

I had to buy this book for a class. I am accustomed to the text book providing me with something useful to study when I am not in class, but that is not the case with this text. There are few relevant worked examples in the book, and the examples it does have are not really reinforced by the exercises. If I can't sit down and look over the examples, and then try to work some exercises, then the book is useless to me. Exercises are scattered throughout each chapter, rather than all being located conveniently at the end of the chapter (like every other normal textbook), so I spend time hunting through chapters just looking for the work I am supposed to do. Problems quite often reference examples or exercises from other chapters. That seems like laziness on the author's part when they could have just as easily copied the needed information. As a result, I spend time hunting through the book to find whatever is being referenced. If you already have some background in mathematical statistics and don't mind the poor organization, this book may be sufficient for you. However, if you are a student wanting to actually learn the material for the first time, you're probably better off buying another book.

What happened to the good reviews I remember reading about this book on this page ? See reviews for 6th edition. A book doesn't go through seven editions if it is a one star book. The criticism that the book references exercises as part of the main stream of the text is true but it is not necessary to work those problems to understand the main text. I never saw a case where the answer given for the odd numbered problem required an even numbered problem (for which answers are not given) as part of the explanation. I'm sure it's true but what is the probability of that happening :) ? Understanding and learning are different. It is not possible to understand a book and learn nothing. This book forces the reader to learn.

If you are taking a math stat class on the level of Casella and Berger this is a GREAT text to use for background (I speak from experience). To get the most out of this book, the level of math needed is a full sequence of undergrad calculus (3 or 4 semesters depending on your university).