The Buckeyes are among the Sweet 16 four years in a row. After the first weekend, my bracket is ranked at 6933 (with 48 points) among more than 1 million brackets on CBSsports.com and the score translates to a 99.5% on ESPN.com. The bracket actually ranked at 2129 after the first round and improved to 1470 after games on Saturday before dropping to the current place on Monday.
It feels good to make good guesses and the better feeling is that I still have all my final four teams and 7 out of 8 elite eight teams alive.
Let’s go, bucks! We will see where my bracket stands after another round.
Posted in Just Numbers, Sports | Tagged Basketball, bracket, Buckeyes, elite eight, NCAA | 1 Comment »
At 7pm on wednesday, 2/6/2013, the weather man is predicting a historical storm that is expected to hit the Northeastern U.S. during the weekend. People in Boston, New York, and other northeastern areas should get ready for it. In this video when meteorologist Chad Myers talked with host Erin Burnett, he used a football play to describe the weather system.
It is a great job that Chad explained the weather (and storm) forecasting in such a simple and understandable way. At the end, Erin and Chad has an even more interesting chat on our weather foresting models:
Chad: This is 36 hours before the storm even starts. That’s why I’m so wish-washing. I can’t say 1 to 12, because that was what the computer are. The computers are literarily for New York city could be 1 inch, mostly rain, and I’m saying. I will show you what is scary in one second. I will just hold on to the graphics here. Boston on this computer, 21 inches of snow. I think that’s a pretty good number, especially for the burghers. Look at this thing, this computer says, OMG, you know what, it is not going to warm up at all. The rain is not going to happen, it is going to be all snow, 23. So literarily we have 1 to 23 inches for snow possible for New York city. That’s why we have to wait for another computer run before we really understand what going to happen here. I hate to be wish-washing about it, but it is a big storm for somebody anyway!
Erin: But, but, … I guess I’m so confused that you seemed to me that we always knew what the weather would be, right? A few days in advance. Nowadays, every time there is a big storm, we have no idea. Could be big, could be little, what is going on? Why is this because … Climate change? I don’t know. What?
Chad (with smile): It is because over population of models and over thinking it. Now we have so many models. Look at this one, oh, look at this one. Before we had two, Elephant and GM that’s all we had. We look at one and we decide one or the other. Now we had like nine, so we don’t know which one to pick.
Erin: You know what, sometime the plenty of choices is not so great.
Chad: That’s right!
We know less and we are less certrain about our prediction because of “over population of models and over thinking”. NICE!
Watch out for the storm of computation power, good or bad.
Posted in Atmospheric Science, Computation, News Media, Statistics | Tagged Boston, models, New York, northeastern areas, storm, weather | Leave a Comment »
This is the third that I’m reading The Tao of Statistics: A Path to Understanding (With No Math) by Dana K. Keller. Of course, it is not entirely because the book has “Tao” in its title.
As the title suggests, there is absolutely no math in the book. This nice little book opens each chapter with a nice drawing by Helen Cardiff and goes on to explain simple statistical concepts in plain words, plus one or two simple examples. A small proportion of the book can be previewed here. Again, absolutely no math.
I think some descriptions of statistics and statistical concepts in this book are quite helpful, even for people who is doing statistics every day. Some quote from its introduction section:
Statistics are filters on how we see the world. They focus our vision, and they help us to see through the fog. In doing so, they also prevent us from seeing some of what else is there. Stay aware of what is being filtered out, too.
You do not need to know how to calculate statistics to understand what they are telling you.
Averages do not exist from most things. They are only ideas. A truly average person does not exits. The idea of an average is what is useful.
The rest of statistics is no more real than averages. Regardless of how technical a statistic sounds, it is still only an idea that can be grasped by all.
The world of statistics has become technically complex. When someone produces a statistic that you have never heard of or seen before, simply ask what it does and for an example that demonstrates its usefulness.
Ideas lead to understanding. Experiencing the ideas of statistics motives us to develop and deepen our understanding of them.
and some more from the Epilogue:
With assumptions piled high
Armed with knowledge and intuition
Although mistakes can be made
Don’t wager against a statistician
The good thing about the book is that you can flip through it a relaxed afternoon and have a enjoyable reading, but the bad thing is the book does cost a small fortune to buy or rent. I guess partly due to those drawings. Preview it before you check it out from your library, if you can find it there.
Posted in Books, Statistics | Tagged Dana K. Keller, Statistics, Tao | Leave a Comment »
Some interesting comments about statistical softwares and the people who use them from Sean J. Taylor‘s The Statistics Software Signal
What your statistical software says about you (to me):
R: You are willing to invest in learning something difficult. You do not care about aesthetics, only availability of packages and getting results quickly.
Python or JVM languages: You are a hacker who may have already been a programmer before you delved into statistics. You are probably willing to run alpha or beta-quality algorithms because the statistical package ecosystem is still evolving. You care about integrating your statistics code into a production codebase.
Julia: You are John Myles White.
Stata: You are an economist who doesn’t care to code your own estimators, probably because your comparative advantage lies elsewhere. Possibly you are doing sophisticated work with panel data where Stata is the only game in town. You don’t care that you can’t do proper programming because you’re not a programmer.
SPSS: You love using your mouse and discovering options using menus. You are nervous about writing code and probably manage your data in Microsoft Excel.
Matlab: You definitely know what you’re doing and you care about performance. You know Matlab is expensive but you aren’t the one paying for it. You live in a bubble where everyone you know uses Matlab.
Mathematica: You are an aesthete who believes everything Stephen Wolfram says.
SAS: You are an analyst for a large pharmaceutical company, and SAS is all you have ever known. You have a large library of custom SAS macros, so that (clearly) makes you a programmer. That anyone would want to hand-code statistical methods leaves you utterly baffled. If SAS does not ship with a particular statistical method, then it probably isn’t important. (h/t Chris Fonnesbeck)
Seems like there are some truth in them. How about we use these as some kind of prior distribution next time.
Posted in Computation, Reblogging, Statistics | Tagged Matlab, R, SAS, Statistical Software | 1 Comment »
Keeping my current weight has been one of my constant goals (and fight ) Staying motivated is a heck of a job in the business of exercising and dieting. Of course, making it a part of one’s new year resolution is motivating. Taking about resolutions, I just heard this story on NPR: “Can Skinny Models Undermine Your Dieting Goals?“
Posting a picture like this on the fridge might seem like good motivation for weight loss. But scientists say it might instead inspire weight gain. Picture source: iStockphoto.com
The millions of Americans who make New Year’s resolutions to lose weight often have pictures in mind. They’re pictures that have been repeatedly supplied by the health and beauty magazines at supermarket checkout lines. They feature skinny models in bikinis, or toned guys with six-pack abs, and captions about how you could look like this by summer.
“There’s one commercial for a cereal brand which actually targets women that want to lose weight,” said Anne Klesse, a researcher at Tilburg University in the Netherlands. “And in this commercial, there’s a woman who wants to fit in a very nice dress. And to make herself more motivated, she puts a picture of a skinny model wearing this dress on her fridge and on the vending machine.”
However, it has not been clear if these motivating pictures help us achieving our weight managing goals. The story based on a recently published paper, “Repeated exposure to the thin ideal and implications for the self: Two weight loss program studies” by Dr.Anne-Kathrin Klesse in Internal Journal of Research in Marketing, says that those pictures are worse than useless in motiving weight losing. Actually, “the researchers found that it was the skinny model that caused dieters to gain weight“.
This does not seems straight forward to me. The research provides one potential explanation:
“Being constantly exposed before and after eating, every time I am writing in my diary, I am reminded of a very skinny model, the idea comes up that it is not attainable for me,” Klesse said.
I was not sold on the argument. In addition, I was wondering why such a topic is published in a journal about marketing, instead of something like health or psychology. So I decided to see how the experiment is conducted to reach these conclusions. Continue Reading »
Posted in Data Analysis, Health, Marketing | Tagged health, Marketing, skinny model, weight losing | Leave a Comment »
The 2012-2013 NFL regular season games were in the book now. Following the fun of comparing expert picks, algorithmic prediction and crowd prediction of the last (2011-2012) season, let’s check how well they predicted this time. Some background information: Accuscore is based on simulations (algorithms and data) by accuscore.com and Pick’em is the average of all predictions by NFL fans who submitted their picks on ESPN.com before the game (kind of a “crowd prediction” by non-experts).
The first noticeable difference is that ESPN added a couple of experts to their pool, from 8 up to 12 by adding Jackson, Johnson, Ditka, Carter (to get a better crowd of experts?)
For the 2012-2013 season, twelve experts’ prediction accuracies range from 60.2% to 69.9% with the median around 64.6%, roughly the same as the median accuracy 64.1% of eight experts in 2011-2012 season.
Pick’em tied accusore with 68% accuracy, better than all experts, in 2011-2012, but both clocked in much lower for the 2012-2013 season. Pick’em achieved 65.9%, slightly beating 8 out of 12 experts, while accusore was worse than 7 experts. Now what do we say about crowd prediction and algorithm prediction?
By the way, it seems Wickersham is the best expert for prediction and did his homework. Way to go!
For statisticians, are these percentages differ significantly?
Posted in Data Analysis, Data Mining, Sports, Stories by Numbers | Tagged accuscore, data mining, expert picks, NFL, prediction, Wisdom of the crowd | Leave a Comment »
The WordPress.com stats helper monkeys prepared a 2012 annual report for this blog.
Here’s an excerpt:
600 people reached the top of Mt. Everest in 2012. This blog got about 9,600 views in 2012. If every person who reached the top of Mt. Everest viewed this blog, it would have taken 16 years to get that many views.
Click here to see the complete report.
Posted in For Myself, Just Numbers | Tagged 2012 | Leave a Comment »