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Tourna String Meter
Tourna String Meter
Price: $22.73
6 used & new from $19.32

4.0 out of 5 stars A pretty interesting gadget. However, somewhat unclear of how accurate it is., June 24, 2015
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This review is from: Tourna Stringmeter (Sports)
My racquet was supposedly strung at 40 pounds. The String Meter came out at 40 pounds for the long strings, but only 30 pounds for the cross strings. I took a few measurements from various tennis friends racquets. More often than not, the measurements were rather unsettling and seemed to have little correlation with the supposed racquet string tension they had ordered from the stringers.

I think this gadget works really well in terms of measuring the string tension of your racquet over time. You can tell how much tension your strings are loosing over time. But, in terms of checking the accuracy of your friends racquets I am not too sure how well it works.

What I get from this is that racquet stringing is a very imprecise art. Let's say you order string tension of 50 pounds with the exact same racquet and same strings from several different stringers. I have no doubt there will be marked differences between all of them. I suspect this difference could be associated with a very wide range. Between the lowest and highest you well could have a difference of 20 pounds (you order 50 and you get a low and high of 40 vs 60). The second issue is how do you measure this accurately. That's a tough one. I am not sure how precise the Stringmeter is.

Another weird finding is that I observed that the cross strings often come out at far lower tension than the long string. This is counterintuitive. From a physics standpoint you would think the opposite be true. A shorter string is much less flexible, and returns greater resistance to any torquing than a longer string. However, in essence the cross strings and the long strings make for two completely separate string jobs. Is there something in the standard stringer technique that make it so (that cross strings are associated with lower tension)?


Fixed Gear Fixie Pedals Foot Strap - Pedals and Straps
Fixed Gear Fixie Pedals Foot Strap - Pedals and Straps
Offered by CyclingForLess
Price: $19.95

5.0 out of 5 stars Excellent for road biking, bike touring, June 24, 2015
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I have an old but very good touring bike. And, I had old pedals with hard plastic cage. Those pedals were too long as they overlapped with the front tire way too much. If my front wheel just wiggled only 5 degrees, my pedals would touch the front tire and disaster would happen. As a result, I suffered about one bad crash a year. The last one really banged me up. My elbow, knee, and one of my ankles were thoroughly scratched and bleeding after hitting the pavement at 14 mph. I needed a solution to keep on biking with that specific bike.

I have no interest in buying biking shoes with toe clips. But, I still wanted an artifact that would give me good torque when riding my touring bike so I kind of can keep up with the toe clip crowd and also climb hills using my muscle power as efficiently as possible. I also wanted a set up that would minimize if not entirely eliminate my "overlap" problem.

Those pedals are an ideal solution. They make riding very comfortable. They are far more comfortable than my former ones. They also do provide good leverage and torque power. I was also able to entirely eliminate the overlap with my front tire. In terms of adjustment, they provide much more flexibility and precision than any other type of pedals including toe clips. I don't think with the latter I could have eliminated the overlap as well as I have with those pedals.

In any case, those pedals are greak for bike touring and to resolve the issues I have dealt with. I am now far more comfortable and safe on my bike than I was before.


Reading the Comments: Likers, Haters, and Manipulators at the Bottom of the Web
Reading the Comments: Likers, Haters, and Manipulators at the Bottom of the Web
by Joseph Michael Reagle
Edition: Hardcover
Price: $17.08
48 used & new from $8.90

5 of 6 people found the following review helpful
3.0 out of 5 stars Well researched but ultimately rather dry, June 24, 2015
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This book is well researched and written, and informative. It refers to a broad litterature of social studies on the topic. Yet, it is rather dry and somewhat boring. I am not convinced the subject makes for a parsimonious 185 page book. A 10 page essay in The Atlantic, New Yorker, or Harper's may have been more appropriate. The effort of the author to broaden the subject by often going way back in history is not always successful. Such efforts sometimes come across as fillers to reach a certain targetted length to make this a marketable book.


Ensure Active High Protein Nutrition Shake, Milk Chocolate, 8 oz,  24 Count
Ensure Active High Protein Nutrition Shake, Milk Chocolate, 8 oz, 24 Count
Price: $41.37

4.0 out of 5 stars Tastes good, reasonably good nutrition. But, not to be confused as an energy drink., June 8, 2015
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I have been a big fan of Ensure products for a long time. I bought many of their products with either 250 calories or 350 calories with lots of carbohydrates. This is actually a really good thing when you need to refuel and need a boost of energy in the midst of doing vibrant exercises. This new high protein drink has a different focus. It is not so much an energy boosting drink, that by definition should be dominated by carbohydrates. It is instead a protein dominant drink. As such it is a good drink after you have done exercises and you need some proteins to restore the health of your muscle tissues. It is a bit lighter in calories with 160 calories, but is pretty sating. Proteins tend to do that (make you feel sated). The drink tastes good. It does not seem to have too many uncalled for ingredients. And, as usual it has a boatload of vitamins and minerals. I think of it as a yogurt shake with more vitamins and supplements. Occasionally, when you are running late for work it could substitute as a reasonably good breakfast. Of course, that's probably not something you should do more than once a week as is true for any types of those drinks. They are great time savers. When doing sports or after sports they can come in real handy. But, you should not confuse them for sustainable nutrition on a consistent basis and forever going forward. This is not a criticism of such products. It is a realistic assessment of their place in terms of your overall allocation of caloric intake. They fit a special small niche of time and place as defined. And, as is they are pretty cool drinks.


Abundance: The Future Is Better Than You Think
Abundance: The Future Is Better Than You Think
by Steven Kotler
Edition: Paperback
Price: $9.22
101 used & new from $3.91

3.0 out of 5 stars Good overview of positive trends with many pipe dreams, April 12, 2015
This is an interesting book that has two main parts. The first part consists of the narrative of the book that supports Diamandis position that things in general are a lot better than they were, and they will continue to get better for various reasons he explicitly defines. The second part of the book consists of a very expansive section of visual data where he shows many graphs capturing some of the trends described in the first part of the book as well as many others.

In the first part of the book, Diamandis sees three major forces that are shaping the world.

The first force is the rise of the bottom billion, the poorest out of the seven billion on Earth. They represent a force because due to the penetration of cell phones, they are leapfrogging into first world technology. In Africa, thanks to cell phones, banking is nearly as evolved and mobile than it is in the first world. This has implications in terms of economic growth, rising living standard for Africa.

The second force is the emergence of what the authors call the techphilanthropists. The pioneer and iconic model of such special individuals is Bill Gates who has dedicated the second half of his life to making a difference and revolutionizing the field of philanthropy. He has used his genius in business and technology to render philanthropy far more accountable, and performance and targeted oriented. As a result, he has rendered philanthropy far more impactful. He also has lead many other similarly oriented billionaires to follow in his footstep. Many have stated that Bill Gates and company have had more impact on several major global issues than even huge supranational entities like the UN and the World Bank. The latter have been good at throwing large amount of money at problems. They have been good also at literally throwing that money away. Indeed, they have often been very ineffective at actually solving any problems they have targeted. And, that’s where the supranationals can learn a lot from the performance driven billionaire-philanthropists.

The third force is the Do It Yourself innovation movement (DIY) whereby individuals can now make contributions even in the most advanced technological domains. This is the essence of the Open Source movement. Open Source projects have had dominant impact within their respective domains from Linux (operating system), R and Python (quantitative software), to Wikipedia, the most successful repository of knowledge in history. Also, individuals may participate in various contests where corporations offer huge monetary rewards for the development of the best app, solution, or program for resolving very challenging problems.

Diamandis has a precise target in terms of global betterment: “a world of nine billion people with clean water, nutritious food, affordable housing, personalized education, top-tier medical care and non-polluting, ubiquitous energy.” And, all of that within 25 years. I gather there are some paradoxes in this forecast. First, we will most probably not reach 9 billion within those 25 years (and that’s a very good thing). Also, the non-polluting, ubiquitous energy seems to be completely Utopian. We now live in the era of an oil & gas revival with the fracking, shale oil and tar sand booms, whereby the United State is soon becoming the largest producer of such compounds ahead of Saudi Arabia. Canada is also a huge producer of those new alternative sources of fossil fuels. This surging supply from North America has caused oil prices to plummet over the past year. All the renewables that Diamandis is so excited about will probably play a very secondary role to the energy mix powering our civilization. Up to 2035, the International Energy Agency forecasts that oil, gas, and coal will remain just as dominant in terms of our overall energy mix as they are today.

Moving on to the second part of the book, the visual data captures a bunch of very interesting trends. For one thing, the advent of the Internet/Worldwide Web becoming accessible to the masses in the early 1990s is associated with several positive trends. One of them is the very rapid drop in the rate of rape in the US (pg. 249). Another positive trend is the very rapid decline in the magnitude and frequency of armed conflicts (pg. 251). Is the association of the reduction in rape in the US and armed conflicts worldwide with the advent of the Internet solely due to randomness? It probably is. But, it is a quirky coincidental observation.

The spread and capability of technology is exploding (pg. 263) associated with plummeting costs in bandwidth ($ per 1,000 Mbps), computing cost-performance ($ per 1 million transistors), and cost of storage ($ per gigabyte (GB).

In my view, one of the most important trends (maybe nearly as important as any of Diamandis three major forces) is the rapid plummeting cost to launch an Internet start-up from $5 million in 2000 to only $5,000 in 2011. That’s a trend that Diamandis observed within the second part of his book associated with visual data. But, he did not register it in the narrative portion of his book (one could include it in the DIY movement; but, it is different). Over a decade ago, to start such an enterprise one had to raise some venture capital to invest in a lot of hardware, software, and expensive computer coders. Now, the costs are so much lower because much of the hardware, software, and even computer coding capabilities can be rented for very little from the clouds. Thus, the cost of starting such a start-up is only 1/1000th of what it was just a decade earlier. That is an amazing increase in productivity. This new business innovation model allows for 10s of different iterations to refine an Internet-based model instead of spending $5 million on one single business idea with a very high risk of failure. It makes entrepreneurial risk so much easier to bear, as it ultimately reduces this risk by dividing it by a high multiple (the large number of iterations). This has to translate into an amazing boost in innovation.

Diamandis shares a few trends that have been popularized by the works of Richard Florida (the author who popularized the concept of the “creative class”). Those include the dramatic drop in the % of the labor force engaged in agriculture that dropped from over 90% in 1800 to less than 2% today (pg. 265). Another Richard Florida-trend is the ever-rising % of the population that lives in cities that is expecting to reach 70% by 2050.

A most challenging trend is that Africa is expected to account for the majority of the population growth (about 2.5 billion out of the next 3.2 billion). It is also the continent associated with many environmental constraints including water, food, and presence of many diseases reaching near pandemic proportions ranging from AIDs to Malaria. Also, chronic civil wars with religious, sectarian, and ethnic dimensions plague this continent. Will cell phones, vertical farming, smart grids, water plant desalination have much of an impact and render Africa more livable with an extra 2.5 billion people than it is today?

As shown, Diamandis take on the future is most always upbeat, and as demonstrated not always realistic or convincing. Often the projections he makes are contradicted by the most credible bodies in the field, like the IEA on energy production mix.


Big Data Revolution: What farmers, doctors and insurance agents teach us about discovering big data patterns
Big Data Revolution: What farmers, doctors and insurance agents teach us about discovering big data patterns
by Rob Thomas
Edition: Paperback
Price: $11.99
56 used & new from $9.00

2.0 out of 5 stars It is about Big Process, not much about Big Data, April 9, 2015
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This book has numerous weaknesses. If you search for “Big Data Revolution” within Amazon, you will see probably half a dozen books with an identical or very similar title that have already covered this topic up to several years ago. And, I know first hand at least one of those books is far better than this one: “Big Data: A Revolution that will transform how we live, work, and think” by Viktor Mayer-Schonberger. Other reviewers have also made this exact same observation. I don’t think this book explains that well the advantage of Big Data (more on that soon). It does not cover at all Big Data analytics. So, if you want to find out a bit about Machine Learning, Clustering, Spatial regression, A/B testing, etc… you will get none of that here.

The authors kind of hype Big Data strategies as inherently superior to traditional business strategies. But, they often trip in effectively supporting this argument. Just sharing a couple of examples below.

The authors describe (pg. 64) the supposedly superior business model of Karolina Zmarlak, the dress designer in the following fashion (no pun intended). Her business sends each customer five different personally customized clothing items to try on at home. The customer is free to pick just one of those and return the other four. That translates into a systematic return rate of 80% with customized items the business can’t readily resell to anyone else. Yet, on page 65 the author states this superior business model allows this company to sell 90% of its inventory at full price every month “because of personalization.” This just does not add up. Earlier on page 60, the author describes this business as having 100,000 clients globally, each spending $5,000 to $10,000 on her clothes. Those figures are hard to believe. Are the clients stores? Or are they individuals benefitting from her personalization-business model? Also, the figures mentioned translate into annual sales of $500 million to $1 billion in annual sales. That’s a lot. Is this correct or is it a typo?

They conclude the first chapter on agriculture by stating “Data trumps weather. Farming and agriculture will be transformed by making the leap of acknowledging this truth.” Throughout the chapter the two authors seemed subject to a bit of magic thinking by advancing that increasing crop yield due to Big Data superior information will readily overcome any environmental constraints to feed an additional 3 billion people expected over the next 50 years. Let’s do a bit of plain arithmetic. The current population is 7 billion. A substantial portion of this population is nourished much below optimal level to reflect good health and a middle class lifestyle. Additionally, a certain portion of cropland is being lost to urbanization and environmental erosion and other factors. Let’s come up with what seems to be pretty benign assumptions: 1) a 15% increase in caloric consumption per capita so everyone has enough to eat; 2) a 10% reduction in crop land within the next 50 years. Now, we know the population is expected to increase by 43% (from 7 to 10 billion). Given that, what is the needed increase in crop yield required to feed everyone given the assumptions mentioned. The solution is: [(1 + 43%)(1 + 15%)]/(1 – 10%)] – 1 = 83%! Can Big Data really increase crop yields between now and then by 83%? Looking at the graph the authors share on the subject (pg. 19), I roughly calculate a 34% increase in crop yield between 1961 and 2005. In view of that, I find the authors’ conclusion not convincing. The more appropriate metaphor is a quote from Benjamin Franklin they share (on pg. 110) “When the well is dry, we know the worth of water.” Nevertheless, things may all work out. World population may stabilize at 9 billion instead of 10. Cropland may not lose ground, but instead gain ground due to Global Warming rendering much of the upper North Hemisphere fertile. Vertical indoor farming, drip irrigation, water plant desalinization may all become more efficient, cost effective, and more prevalent. Agriculture of water-intensive crop may be substituted for more water-efficient ones. Related distorting water subsidies may be reduced. Human diet in general may become more water efficient (moving away from cattle). But, all those potential trends that may save the World, as defined, have not a whole lot to do with Big Data.

If one would drill down further, there are probably other similar examples where the authors misrepresent the benefit of Big Data. However, just moving onto generic errors. There are a few and they are big ones. On pg. 34, the authors mention US healthcare spending runs at $1.2 trillion. No, it runs at over two times that at close to $3 trillion (17% of the $17 trillion US economy). On page 135, they completely miss the concept of Bayesian probability as they state that it requires knowing the underlying probabilities perfectly. No, it does not. And, that is why it is so revolutionary and was formerly controversial as it was rejected by the traditional statistical establishment (frequentists) who did require that you knew probabilities perfectly. To the contrary Bayesians did not. On pg. 39, they state that Machine Learning is better than regression, but they never explain why (they neither explain what is Machine Learning nor Regression). This is an error of omission.

So far you have to wonder why am I giving this book more than a 1 star rating? Well, the book is not all bad. It has some information if you can overlook all the mentioned shortcomings. The authors impart information on the processes associated with implementing Big Data infrastructure within a business. They have a poor habit of calling a Big Data process a Big Data pattern, which is misleading. But, once you overcome this obfuscating semantic, there is some information to capture from the authors. This information regarding the implementation of processes to develop a good Big Data infrastructure is presented in three chapters (10, 12, and 16). In chapter 10, the authors outline five sequential steps to capture, extract, and classify data. They are: 1) Data acquisition; 2) Pre-processing; 3) Feature extraction; 4) Classification; and 5) Post-processing. In chapter 12, they describe an exhaustive 54 processes and subprocesses that they tie to earlier narrative examples within the book. And, in chapter 16, they provide a method on how to categorize and implement those 54 processes into seven concrete steps: 1) Understand data assets; 2) Data exploration; 3) Design the future; 4) Design a data-driven business model; 5) Transform business processes for Data era; 6) Design for governance and security; and 7) Share metrics and incentives.

The above information is relevant for business managers in charge of developing a Big Data capability within their business. For everyone else, this stuff is much less relevant and is not representative of what other books have better described as the “Big Data Revolution.” On this count, this book is really mistitled.

Also, the information imparted on the mentioned Big Data processes is not efficiently imparted. One can probably get all that information far faster by reading a couple of papers on the subject.

The subtitle of the book is also plain wrong. Reading the book, the authors make a good case that farmers, doctors, and insurers are much lacking in Big Data knowledge. And, they illustrate how Big Data could revolutionize those fields. The implication for doctors is very interesting. Either they will have to become mathematicians to understand Big Data output. Otherwise, doctors will become irrelevant as Big Data (through specialized web based vendors and apps) will provide the evidence-based answers that doctors have ignored so far.


Data Science For Dummies
Data Science For Dummies
by Lillian Pierson
Edition: Paperback
Price: $22.22
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8 of 8 people found the following review helpful
4.0 out of 5 stars 5 for definition 3 for tutorial, April 5, 2015
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Lillian Pierson has written a remarkable book on one count (explaining Data Science) and a not so great one on another count (actually teaching the methods of Data Science). Based on the latter the usual audience of the “For Dummies” series may be somewhat disappointed.

After studying this book, you will not have developed the adequate proficiency to undertake any of the quantitative, programming, or spatial visualization methods the author presents. This is not surprising. You won’t readily pick up the entire fields of probability, regressions, and Monte Carlo simulation in the barely 20 pages that the author assigns to this matter. Similarly, you won’t be able to program much if anything by reading the 20 pages chapter on either Python or R. As you know, there are entire For Dummies books written on many of those subjects separately. And, many of those actually struggle to cover one single field adequately. For instance, in my view the “R for Dummies” and “Probability for Dummies” are mediocre (after studying them I rated both of them a 3). So, if others could not well cover R over 350 pages, what chance would Pierson have to cover it adequately in 20 pages.

However, this is not to say that this book does not have much value. It really does. And, that is in defining what Data Science is, in exploring all its aspects, in differentiating between Data Engineers and Data Scientists, in surveying practical solutions of how many fields are currently using Data Science and how, and in informing you what exists out there.

Additionally, some of the chapters are surprisingly strong (relative to some of the quick overview of specific subjects as demonstrated by other chapters). This includes many chapters near the end. Chapter 19 on Environmental Data Science is excellent. You can tell this is one of the author’s subspecialties. Chapter 20 on Data Science for driving E-Commerce is also excellent. The author has a surprisingly broad and deep culture on this specific topic. The Part of Tens is very good. Sometimes within the Dummies Series, the Part of Tens are wasted fillers mandated by the For Dummies format. This is not the case here, as this section is very informative with many excellent free data sources and data tools.

In summary, the book is excellent in defining what Data Science is. It is not always that good at teaching how to practice Data Science. As indicated above, this is no fault of the author. You can’t possibly teach a dozen complex mathematical disciplines, computer programs, and data visualization techniques in 350 lightly written pages. As mentioned, it is already remarkable that in that small pace she could survey and define so well all those disciplines (and give you a rudimentary idea of how to practice those).

The book can be used in many different ways.

For many readers it may serve as a pretty good benchmark of where they stand. If one is reading this book, it is not unlikely that you are already half way there to being a Data Scientist. This book will clearly spell out what are the skills you already have that qualify you as a Data Scientist, and what skill you are still missing to be a full fledged one.

This book can serve as an excellent stepping stone to further study many aspects of this fascinating field. Pierson has a very deep and broad culture in Data Science. She refers to tons of stuff, including very powerful open source web based data visualization and data analysis software that most people (even quants themselves) may have never heard off. She also defines well how to use certain computer programs and what they can do for you. Thus, if you are hesitating between learning R or Python, she will provide much valid information on the subject to facilitate your decision. In other words, Pierson provides an excellent survey of what exists out there, and what fields, methods, or programs are out there that you could study next. That alone is a huge value and is the main value of the book.


The Other "F" Word: How Smart Leaders, Teams, and Entrepreneurs Put Failure to Work
The Other "F" Word: How Smart Leaders, Teams, and Entrepreneurs Put Failure to Work
by John Danner
Edition: Hardcover
Price: $19.08
59 used & new from $11.07

3 of 3 people found the following review helpful
5.0 out of 5 stars Very insightful, March 15, 2015
The authors promote a culture of educated risk-taking & learning from failure. Such corporate cultures are in short supply in our ultra-cautious post-financial crisis era. The two authors have walked the talk for a very long time. Their impressive collective background brings much credibility to their writing. They have had professional positions as organizations’ leaders, senior corporate executives, entrepreneurs, angel investors, management consultants, and business school professors.

In addition to their extensive first hand experience (within the Preface, with humor and candor they share some of their failures under the section “Our Failure Bona Fides”), they have done an abundance of research on the subject. This research included reviewing many relevant studies and interviewing a long list of Who’s Who in business, high-tech, innovation, venture capital, and overall corporate leadership.

This is a serious business book earmarked for senior executives and entrepreneurs who do have some control over their respective corporate culture or less senior individuals such as MBA students who aspire one day to be senior executives.

The authors provide upfront effective tools on how to handle and learn from failure at the organizational level. In the first few pages of the book, they define and diagnose failure over 10 key points. One of those points fleshes out a detailed framework on how to explicitly learn from failure: “Failure Value Cycle.” And, this detailed framework represents the essence of the most pragmatic section of the book (Part III chapters 9-17).

Backtracking, Part I defines what failure is with many facts and trends across different industries and activities. Part II shares many real world examples on how numerous businesses have managed to learn from failures (not always well as you can imagine). The authors explore business performances in terms of learning from failure at three very different stages with very different failure implications: start-ups, young but established businesses, and mature and large organizations. Part IV is a concluding section that lifts the main points from the book on how to create a “failure-savvy organization.”

Back to the tools, the authors share one really useful one: a Report Card (page 185). By just asking five straightforward questions this Report Card nails down the corporate culture of a workplace. As you read those questions, you may feel like your workplace has much room for improvement regarding the learning from failure bit.

This book is rich in examples, insights, and applicable tools. Within this review, I have given the audience just a quick 10,000 feet-high perspective on what this book is about. As you land down to earth’s level, there is a lot more to it. The authors write in a very user-friendly and entertaining way. If you are interested in this subject, I venture that you will very much like this book.

If you have read and enjoyed books like Clayton Christensen “The Innovator’s Dilemma”, Peter Thiel “Zero to One,” or Shane Snow “Smart Cuts” you will most probably find this book equally interesting.


Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences)
Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences)
by John Fox
Edition: Paperback
Price: $14.15
47 used & new from $6.99

2 of 3 people found the following review helpful
2.0 out of 5 stars Very obtuse and incomplete, February 12, 2015
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This book, given its focus on a narrow subject, is actually pretty long. 90 pages to cover regression diagnostic is a long slog. Yet, the author sates that "because of space considerations... there is no treatment of [autocorrelation]." That is a huge gap as the independence of the residuals is one of the main assumptions of the linear regression model. The author admits that much when he describes the corresponding Gauss-Markov theorem on pg. 40.

Throughout the book there were so many math notations that were undefined that I could not clearly understand what the author was conveying. For instance, on pg. 16 he talks about the Mellows stat associated with the terms Cp and p. Neither are clearly defined. I guess p is number of independent variables (a guess at best). But, I have no idea what Cp stands for. Later on page 24, he defines the h or hat value in a less than transparent way. On page 28, m is undefined. On pages 68, 87, and 89, NID is undefined. On page 89, V is undefined. Given that those terms play important roles in many equations throughout the book, I could not grasp their meaning.

The narrative itself is often perplexing. For instance, on page 25, the author states: "... even if the errors have equal variances..., the residuals do not." Given that residuals and errors represent the same thing, this quote is not readily comprehensible. There are other somewhat confusing sentences within the book.

Occasionally, I came across tests I knew and calculated long hand in Excel. Yet, as he described them I could hardly recognize them. This includes the Breusch and Pagan test and White test on pages 73, and 74.

Two basic guides to overall econometrics do a good job of covering the entire subject including diagnostic tests in a far more transparent way. Those are: Econometrics For Dummies by Roberto Pedace and a A Guide to Econometrics - 4th Edition by Peter Kennedy.


An R Companion to Applied Regression
An R Companion to Applied Regression
by John Fox
Edition: Paperback
Price: $66.51
54 used & new from $50.78

3.0 out of 5 stars Good but probably harder than necessary, January 22, 2015
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Within the book, Fox describes extensively all the relevant capabilities of R and his `car' package as well as other related packages. The book covers more than you think anyone would know about regression analysis, model testing, and graphics. It serves as an encyclopedic reference on the topics. Each chapter is pretty much stand-alone. So, you can read those separately to learn what you need.

However, be warned this book is pretty hard. Much of the coding is challenging. I have attempted to replicate some of it; And, often without success getting repeated "Error" messages. Another reviewer criticized the author for using more complicated coding than necessary. And, I concur with this assessment.


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