The Manga Guide to Statistics Paperback – Dec 8 2008
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About the Author
Shin Takahashi graduated from the Graduate School of Design at Kyushu University in Japan. He has worked as a lecturer and as a data analyst and is currently employed as a technical writer. Takahashi has published several books in the Japanese Manga Guide series, including Statistics-Factor Analysis Edition and Statistics-Regression Analysis Edition (both published by Ohmsha).
Trend Pro, Inc. is a pioneer of Ad-Manga--advertisement and advertising using Manga--in Japan. The company has produced over 1,700 Ad-Manga for over 700 clients, including many well-known public companies and government agencies. The company has over 100 registered professional Manga artists.
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Top Customer Reviews
This isn't a knock on those other titles, which are excellent. But somehow these subjects and this format are just a perfect fit for each other. I need a quick refresher on a particular technique, it's explained clearly and a small number of sample questions are given. For someone who knows the material and needs a refresher, these guides can't be beat. For students studying them for the first time, I would pair these slim guides with a thicker textbook or problem set for practice (the one thing these guides don't have) for an unbeatable combination.
I’ll be blunt. I took one statistics class when I was in college. I hated it. In fact, I never finished it. Please understand when I say “I have an aversion to statistics as something I have to actually do”, I am not kidding. as a software tester, that puts me in a bit of a bind. If I can’t make some sense of the data I receive, I can’t do as effective a job. at best, I need to farm that work out to someone else on my team who can do the statistical analysis, meaning I need to get their take and explanation to make decisions. that causes delays. Overall, it would be better to just suck it up and learn a bit about statistics. It’s a core piece of domain knowledge any good software tester should possess, if not immediately, then at some point in their career.
There are lots of ways to learn about Statistics, and frankly, most of them are a bit painful. College courses, text books, online videos, etc. can help, but they are often slow, or assume that you have some background in the ideas already. What to do when you want to get the gist of the idea before you tackle the hairier details? that’s where “The Manga Guide to Statistics” comes in handy.
A caveat: this should absolutely not be your only guide to learning statistics.Read more ›
Most Helpful Customer Reviews on Amazon.com (beta)
The Manga Guide to Statistics does similar things but a little differently. This book is in cartoon strip form and the characters are familiar to many kids who these days wacth the Japanese cartoons on television and read the comic books. This includes my son Daniel who is a high school junior. Dan hates to read but loves math and science and this is the first statistics book that intrigued him enough to read it! I know is reading it and enjoying learning from it by the questions he asks. So like the other cartoon book on statistics this too is a gentle introduction for those with math skills and those with an aversion to mathematics. It shows how statistics is practical by illustrating the techniques on everyday real world data, such as the scores of bowling team players at a bowling alley. It covers the basic summary statistics, correlation, hypothesis testing and probability distributions. What I found interesting was that in addition to the ordinary Pearson product moment correlation they also provided intra-class correlation and Cramer's V (for categorical data). These methods are rarely covered in elementary texts.
One thing it has that is missing in "The Cartoon Guide to Statistics" is the teaching of how to use the computer to apply what they learn. In the final chapter they do this using Excel and teaching things step by step using screen shots of excel spreadsheets.
Throughout the book when a new statistic is introduced they go through the step by step details of the calculations. This is something that student do not necessarily need to learn in the age of computers and statistical computer packages. However, going through the tedium of the calculations has a way of reinforcing the concepts and it gives the student a better understanding of exactly what a variance and a standard deviation are.
I recommend this book for high school students to supplement what they learn in class or for independent self-learning. College student with weak math backgrounds who need an introduction to statistics may also find this book useful and interesting. It is working wonders for Dan who now wants to get the soon to be published Manga guides to physics, calculus, microbiology and databases! Unfortunately this one is the first to come out and the others won't appear until later in 2009.
Still, maintaining interest and good teaching, while related, are not identical. One can maintain interest in ways that detract from learning as well as in ways that enhance learning.
The tendency in this text to oversimplify (e.g., the discussion of what is and is not "measurable" at the beginning of the book, the underemphasis of the importance of random selection) are definite negatives. They will lead a learner with no background in the use of statistical procedures to mistaken conclusions about the meaning of measurements and the generalizability of findings.
In at least one case, the oversimplification proceeds to the point of presenting information that is wrong (i.e., the examples of alternative hypotheses on pp. 172-173). To be fair, there are many "gentle" statistics texts that, as does the Manga Guide to Statistics, present the notion that the alternative hypothesis is simply "not the null hypothesis."
Despite the popularity of this view, Neyman and Pearson (who developed statistical hypothesis testing theory 75 years ago) noted that the "not the null" formulation of the alternative hypothesis would lead to the acceptance of trivial effects as meaningful simply because they were "statistically significant."
The "not the null" formulation of the alternative hypothesis creates other problems.
For example, the null hypothesis on page 173, "The allowances of high school girls in Tokyo and Osaka are the same," has as its alternative, "The allowances of high school girls in Tokyo and Osaka are not the same." Stating the alternative hypothesis in this way does not permit an evaluation of the power of a statistical test (power refers to the probability that a test will detect a difference, change or relationship when it is present). As Neyman noted, since the test would have to detect an infinitesimal difference, the power would necessarily be infinitesimal as well.
Instead, an alternative hypothesis should specify a minimum effect, e.g., "The allowances of high school girls in Tokyo and Osaka differ by an average amount of at least ¥500." By specifying a minimum effect to be detected, we can find the probability that a statistical hypothesis test would detect a difference of at least ¥500 (the test's power).
Since I have to devote time to "unteaching" the "not the null" formulation of the alternative hypothesis, I am far from thrilled to see it here. Convincing learners that the easily understood "not the null" definition is wrong usually requires a lot of work and pain.
After all, who likes being told that what they thought they understood, is what they still do not understand?
This makes it more difficult for me to help my students understand the central importance of power to statistical testing. And, as Neyman pointed out, the power of a test is the main determinant of how useful it is.
It may seem that I am asking too much of an introductory text.
I do not think so.
It is my experience that one must engage in some fairly sophisticated reasoning to understand the meaning of the results of a statistical analysis. The simple, obvious interpretation is almost always wrong (cf., Darrell Huff's How to lie with statistics).
We do a learner no favors by simplifying a complex process to the point where we deceive the learner into thinking that they understand something that they do not.
The trick (which I am still working on mastering) is to help learners learn how to enjoy the challenge of minimizing, but still living with, uncertainty (an important element of all statistical reasoning) and also to help them learn to be suspicious of "easy" answers.
I recently got around to reading W. Edwards Deming's book, Out of the Crisis. In it, he made an observation about maintaining learner interest and quality teaching that is relevant to this book: "In my experience, I have seen a teacher hold a hundred and fifty students spellbound, teaching what is wrong." The Manga Guide to Statistics held my interest from the moment I started reading it. In fact, I read it in one sitting. I honestly enjoyed reading it, but it is wrong in too many places.
I purchased the Manga Guide to Statistics thinking that I might use it in my introductory research methods courses. I shall not use it. I shall not recommend it. I shall not mention it.
Note: I apologize for the lengthy discussion of the alternative hypothesis. I am afraid that I am not clever enough to find another way to demonstrate the problem of oversimplification.
Deming, W.E. (1986). Out of the Crisis. Cambridge, MA: MIT Center for Advanced Engineering Study.
Huff, D. (1954). How to Lie with Statistics. NY: Norton.
Neyman, J. & Pearson, E. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 231, 289-337.
I never thought I'd say this, but the authors have made a book on statistics FUN without dumbing it down (this effectively covers at least the entirety of a college level stat intro class).
As a student, this cleared up many problems I'd been having operationalizing fairly advanced formula within Excel. The chapter on inputting statistical formulae in Excel is amazing and worth the cost of the book in itself. The explanations of the formulas use concrete, real world examples. No gambling examples or other unnecesarily abstract or standard scenarios.
As a teacher, I bow down to Mr. Takahashi and the folks at Trend-pro. Their pedagogical expertise is unparalleled. I can only hope that one day I am 1/10th the teacher this man is. He made statistics, a fairly dry subject, not just palatable, but entertaining.
The short answer is yes. The is a deceptively simple introduction to statistics that is taught via manga, or Japanese cartoons. If you ride the subway in Tokyo, you'll see many riders reading manga for diversion on their way to and from work. They are serial stories presented in black and white cartoons.
The Manga Guide to Statistics uses a cartoon format to present elementary statistics. You might think that an apparently non-serious approach wouldn't work in introducing a complicated subject such as statistics, but think again. The basics are all here. Chapters are included on the subjects listed below
Categorical & Numerical Data defined
Various descriptions, mean, median, standard deviation, estimation theory
Standard Score, Deviation Score
Probability density function
Standard normal distribution
Chi square distribution
Use of computer spread sheets to do the math
Chi-Square test of independence
Null hypothesis and alternative hypothesis
P-value and procedure for hypothesis tests
Tests of independence and tests of homogeneity
Again, use of computers to simplify the calculations
This is a good book for a general introduction to the theory and methodology of statistics. It is short on examples and problems to work on, but for certain readers, it may have value in helping them understand the available statistical tools. It is also short as well on explaining the strengths and weaknesses of statistics, For example, I don't believe you could use just the material in the book to critique the use of statistics in a medical article.
While it has the math, and a short description of the theory, it falls short in teaching the philosophy behind our understanding of statistics. If you don't believe that is important, take a look at the current financial landscape in the world. Many people blame the "greed" of Wall Street for our financial troubles, but a more basic cause is a misuse and lack of understanding about what one can actually learn from statistics. In short, in the real world, there is no such thing as a "normal" population, and the expression P=0 never happens. An admittedly longer and more complex book that should be on the short reading list of all who are interested in "understanding" statistics is The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives by by Deirdre Nansen McCloskey and Steve Ziliak
But for an introduction, it may be very helpful to certain readers, particularly to those who may be visual learners.
Recommended with reservations.
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