Tuesday, June 30, 2009
JMP Workshops for Professors
JMP Users Group Established in Atlanta
New capabilities in JMP 8 and use of the software in classroom and business environments were focal topics at the inaugural meeting of the Atlanta JMP Users Group. More than 30 people attended the June 17 gathering at the SAS Atlanta Training Center. The newly formed group is led by business effectiveness consultant Kevin Holston and includes members from a variety of industries, including healthcare, education, finance, telecom, energy, consumer products and marketing.
The kick-off event featured three well-received presentations. JMP Systems Engineer Mike Vorburger (pictured below) discussed new capabilities in JMP 8. Jennifer Priestley, PhD, of Kennesaw State University presented SAS in the Classroom: Credit Scoring Using JMP. Eric Schmidt, PhD, of Georgia-Pacific discussed Time Series Models and Forecasting in JMP 8.
According to Holston, there was “a lot of excitement and interest in forming the Atlanta JMP Users Group. Many were awed to see the power in JMP 8.”
More than 80 percent of the participants noted that they have some experience with Base SAS programming; 5 to 10 percent have some experience with SAS Enterprise Guide and JMP. “All wanted to know more and each spoke to potential applications in their business environment,” Holston explains. In the future, the group hopes to cover topics such as SAS and JMP integration, data mining (partitioning) and modeling.
Many expressed interest in meeting on a quarterly basis. The next meeting is slated for September or October. A core team of volunteers will soon be recruited to plan meetings and select presenters. The group hopes to leverage social networking tools such as LinkedIn, Twitter and Facebook to spread the word about upcoming opportunities.
The kick-off event featured three well-received presentations. JMP Systems Engineer Mike Vorburger (pictured below) discussed new capabilities in JMP 8. Jennifer Priestley, PhD, of Kennesaw State University presented SAS in the Classroom: Credit Scoring Using JMP. Eric Schmidt, PhD, of Georgia-Pacific discussed Time Series Models and Forecasting in JMP 8.
According to Holston, there was “a lot of excitement and interest in forming the Atlanta JMP Users Group. Many were awed to see the power in JMP 8.”
More than 80 percent of the participants noted that they have some experience with Base SAS programming; 5 to 10 percent have some experience with SAS Enterprise Guide and JMP. “All wanted to know more and each spoke to potential applications in their business environment,” Holston explains. In the future, the group hopes to cover topics such as SAS and JMP integration, data mining (partitioning) and modeling.
Many expressed interest in meeting on a quarterly basis. The next meeting is slated for September or October. A core team of volunteers will soon be recruited to plan meetings and select presenters. The group hopes to leverage social networking tools such as LinkedIn, Twitter and Facebook to spread the word about upcoming opportunities.
Friday, June 26, 2009
Talking Visual Analytics with Stephen Few
Thursday, June 25, 2009
Nuggets of Wisdom from Risk Visualization Expert
Friday, June 19, 2009
Bogeys, Pictures and Numbers
This weekend features one of my all-time favorite sporting events: the golf US Open (plus Father’s Day on Sunday provides a convenient guilt-free excuse to actually watch it). This year, the tournament is held at the Bethpage Black course just outside of New York City. It has a classic sign:
Not sure who “We” represents, but there is an unquestionable tone of authority, with prepositions, definite and indefinite articles all capitalized. And note this course is not just for “Skilled Golfers”, but “Highly Skilled Golfers.” (Hats off to the pros this week who are also battling Mother Nature under very soggy conditions.)
Some data sets should come with a similar warning. In genomics, we are now faced with experiments conducted on thousands of individuals with millions of measurements on each across a variety of complex molecular domains: genetic markers, transcript abundance, copy number, microRNA, protein and metabolite intensities, not to mention thousands of standard phenotypes. Analyzing such data sets properly certainly requires skill along with the best possible software.
A primary goal of JMP since its inception more than 20 years ago has been to provide a dynamic and optimal combination of both statistics and graphics. Drop one of this pair, and you are going to miss something critical. JMP Genomics, although much younger, is definitely building on the same philosophy. Accomplishing this goal is difficult, but we continue to make progress and relish your feedback on how to do it better.
Confession: Every time I pass a mirror and find no one is looking, I practice my golf swing. I’m a sucker for every newfangled idea on how to hit a golf ball better -- trust me, there is an infinite supply of them -- and just have to try it. I’ve been tinkering with my dang swing since graduate school and still don’t have it right. Not seeing him enter, I almost knocked a guy out one time with my mock follow-through in a small men’s room.
One technology that’s a godsend is digital video with slow motion. Interactively successive freeze frames taken from good angles show exactly what I’m doing (even though I think I’m doing something else) -- that, of course, and actually striking that dumb little 1.68 inch diameter sphere and adding up my score.
Pictures and Numbers. One day they might even get me to “Highly Skilled.”
Not sure who “We” represents, but there is an unquestionable tone of authority, with prepositions, definite and indefinite articles all capitalized. And note this course is not just for “Skilled Golfers”, but “Highly Skilled Golfers.” (Hats off to the pros this week who are also battling Mother Nature under very soggy conditions.)
Some data sets should come with a similar warning. In genomics, we are now faced with experiments conducted on thousands of individuals with millions of measurements on each across a variety of complex molecular domains: genetic markers, transcript abundance, copy number, microRNA, protein and metabolite intensities, not to mention thousands of standard phenotypes. Analyzing such data sets properly certainly requires skill along with the best possible software.
A primary goal of JMP since its inception more than 20 years ago has been to provide a dynamic and optimal combination of both statistics and graphics. Drop one of this pair, and you are going to miss something critical. JMP Genomics, although much younger, is definitely building on the same philosophy. Accomplishing this goal is difficult, but we continue to make progress and relish your feedback on how to do it better.
Confession: Every time I pass a mirror and find no one is looking, I practice my golf swing. I’m a sucker for every newfangled idea on how to hit a golf ball better -- trust me, there is an infinite supply of them -- and just have to try it. I’ve been tinkering with my dang swing since graduate school and still don’t have it right. Not seeing him enter, I almost knocked a guy out one time with my mock follow-through in a small men’s room.
One technology that’s a godsend is digital video with slow motion. Interactively successive freeze frames taken from good angles show exactly what I’m doing (even though I think I’m doing something else) -- that, of course, and actually striking that dumb little 1.68 inch diameter sphere and adding up my score.
Pictures and Numbers. One day they might even get me to “Highly Skilled.”
Monday, June 15, 2009
JSL Tip: Use Matrices Instead of Lists
Tuesday, June 9, 2009
Vector Plots in JMP
Vector plots show arrows on a two-dimensional plot and allow one to see four dimensions of data: x position, y position, arrow angle, and arrow length. Equivalently, the four dimensions can be x start position, y start position, x end position, and y end position. The latter form is most convenient for JMP. Though JMP doesn't have a menu command to create vector plots, arrows can be added to almost any plot without much trouble.
While lots of four-variable data sets can be adapted for vector display, I'll demonstrate with one of the most natural applications: wind data. This data set records wind speed and direction for various weather stations in the Chicago area. I used formulas to convert the speed and direction into end positions for the arrows and then added the following graphics script to a bivariate scatterplot using Customize from the graph's right-click menu.
Pen Size( 2 );
For Each Row( Arrow( {lon1, lat1}, {lon2, lat2} ) );
Here's a screen capture showing a little bit of the data table, graph, and customize dialog:
The next steps were to delete the rows with no wind and to turn off the points in the graph.
To provide context, I added the map of weather stations provided by the weather service. Placing a image in a graph is a bit tricky (and could be the subject of another post). There are two options: Draw the picture in another custom graphics script, or drag and drop an existing image into the graph. I chose the latter since I had an image file. First, I sized the graph to be the same size as the image, and then I dragged the image file into the center of the graph. After a few tries, the alignment was pretty good. (BTW, we're working on making this better in JMP 9.)
The plot shows relative wind readings taken at 6 p.m. on May 31, 2009. With the help of the background image, we can see how wind patterns vary in relation to Lake Michigan.
Source of data and image: the National Severe Storms Laboratory Historical Weather Data Archives, Norman, Oklahoma.
Monday, June 8, 2009
Benefitting from the Wisdom of Others
For about a year now, I’ve been having trouble with my faithful Craftsman Eager-1 push mower, which I bought at Sears about 15 years ago. The daggum thing starts up fine but then cuts off after about 2 seconds. This has led me to adopt the following algorithm:
1. Press rubber priming balloon 5 times.
2. Pull rip cord to start engine.
3. If the engine does not start, go to Step 1.
4. If the engine starts but then stalls after a few seconds, utter expletives go to Step 1.
5. If the engine stays on, mow the grass.
6. If the engine cuts off again, utter stronger expletives and go back to Step 1.
My problem is that the number of iterations through Steps 1-4 has been steadily increasing by about 2 times per month. Now that I’m north of 20 times for just one mow, our longstanding relationship is really on the rocks. I think it’s time for a new mower, but what am I to do with this one?
On my way to the store to check out the latest new models (urggh-urggh), I pass a small house with a hand-written sign out front:
Good Used Lawn Mowers
555-1234
“What the heck,” I say to myself as I pop the number into my cell. After a few rings, an elderly voice with a friendly Southern drawl answers:
“Hello.”
“Hi, I’ve been having trouble with my mower and was wondering if you might be interested in it.”
“Bring it by on Saturday at 9 in the mornin’, ‘cause I like to sleep in on weekends. When you come, knock on the back door.”
“OK, see you then.”
On Saturday, I pull around behind the house to find a double-wide carport with what must be the largest assemblage of used hand mowers in the state of North Carolina. Some are obviously very old but are neatly arranged, and all appear to be ready for action.
I knock on the door, and a spry old gentlemen with a flannel shirt and workman pants pulled up over his stomach with suspenders greets me with a firm handshake and beckons me to an overstuffed chair in his small living room. We exchange pleasantries, and I learn he is 91 years old and had spent 45 years as a railroad engineer. He has lived in Cary his entire life, and after his wife passed away, he started tinkering with mowers.
We go out back to take a look at the Eager-1. He deftly removes the air filter and squirts a bit of starting fluid into the exposed hole.
“Give ’r a pull,” he tells me.
I obey, and the Tecumseh engine roars to life but then quickly begins to stall in its usual fashion. But just before it completely dies, he gently places his index finger over the hole and, lo-and-behold, the engine coughs and cycles back to full power! It tries to stall again, but with perfect timing he chokes off the hole just enough to maintain that magic mixture of fuel and air. After a few more taps the engine is running steadily and better than ever. “Give ’r a try,” he says, pointing to a patch of grass. I am so ecstatic that I mow his whole backyard.
As software users and developers, we’re often tempted to abandon and bash old technologies and go for the latest, greatest new thing. While I’m certainly in favor of using the best means possible for the task at hand, sometimes those best means are those that have stood the test of time and have benefitted from the wisdom of those who have struggled through and solved myriads of problems using them. I put classic SAS software into this category – it provides a richly deep and powerful foundation for the processes in JMP Genomics. Our team continues to learn about clever new ways to use it to more effectively handle genomics data.
By the way, using this new-to-me-but-really-old-school technique, I can now start my Eager-1 with a single pull
. It’s still going strong, and so is my nonagenarian mower buddy.
1. Press rubber priming balloon 5 times.
2. Pull rip cord to start engine.
3. If the engine does not start, go to Step 1.
4. If the engine starts but then stalls after a few seconds, utter expletives go to Step 1.
5. If the engine stays on, mow the grass.
6. If the engine cuts off again, utter stronger expletives and go back to Step 1.
My problem is that the number of iterations through Steps 1-4 has been steadily increasing by about 2 times per month. Now that I’m north of 20 times for just one mow, our longstanding relationship is really on the rocks. I think it’s time for a new mower, but what am I to do with this one?
On my way to the store to check out the latest new models (urggh-urggh), I pass a small house with a hand-written sign out front:
Good Used Lawn Mowers
555-1234
“What the heck,” I say to myself as I pop the number into my cell. After a few rings, an elderly voice with a friendly Southern drawl answers:
“Hello.”
“Hi, I’ve been having trouble with my mower and was wondering if you might be interested in it.”
“Bring it by on Saturday at 9 in the mornin’, ‘cause I like to sleep in on weekends. When you come, knock on the back door.”
“OK, see you then.”
On Saturday, I pull around behind the house to find a double-wide carport with what must be the largest assemblage of used hand mowers in the state of North Carolina. Some are obviously very old but are neatly arranged, and all appear to be ready for action.
I knock on the door, and a spry old gentlemen with a flannel shirt and workman pants pulled up over his stomach with suspenders greets me with a firm handshake and beckons me to an overstuffed chair in his small living room. We exchange pleasantries, and I learn he is 91 years old and had spent 45 years as a railroad engineer. He has lived in Cary his entire life, and after his wife passed away, he started tinkering with mowers.
We go out back to take a look at the Eager-1. He deftly removes the air filter and squirts a bit of starting fluid into the exposed hole.
“Give ’r a pull,” he tells me.
I obey, and the Tecumseh engine roars to life but then quickly begins to stall in its usual fashion. But just before it completely dies, he gently places his index finger over the hole and, lo-and-behold, the engine coughs and cycles back to full power! It tries to stall again, but with perfect timing he chokes off the hole just enough to maintain that magic mixture of fuel and air. After a few more taps the engine is running steadily and better than ever. “Give ’r a try,” he says, pointing to a patch of grass. I am so ecstatic that I mow his whole backyard.
As software users and developers, we’re often tempted to abandon and bash old technologies and go for the latest, greatest new thing. While I’m certainly in favor of using the best means possible for the task at hand, sometimes those best means are those that have stood the test of time and have benefitted from the wisdom of those who have struggled through and solved myriads of problems using them. I put classic SAS software into this category – it provides a richly deep and powerful foundation for the processes in JMP Genomics. Our team continues to learn about clever new ways to use it to more effectively handle genomics data.
By the way, using this new-to-me-but-really-old-school technique, I can now start my Eager-1 with a single pull
. It’s still going strong, and so is my nonagenarian mower buddy.
Wednesday, June 3, 2009
Stephen Few's New Book Is a Must-Read
“Now You See It” is data visualization expert Stephen Few’s new book, explaining how to use simple visual techniques for quantitative analysis. In this textbook-sized offering, Stephen explores one of the more overlooked aspects of analysis: the graphic representation of information.
Stephen lays the foundation for good visual analysis in Part I by defining how we perceive information. He states, "…there are ways to visually display data that are effective because they correspond naturally to the working of vision and cognition, and there are ways that break the rules and consequently don’t work. If we wish to display information in a way that will enable us and others to make sense of it, we must understand and follow the rules.”
He goes on to explain that we "…perceive several basic attributes of visual images pre-attentively, that is, prior to and without the need for conscious perception."
Below is a list of those pre-attentive attributes that are quantitatively perceived in and of themselves, without having values arbitrarily assigned to them:
Length – longer =greater
2-D Position – offset higher/lower, or left/right=greater
Width – Wider=greater
Size – Bigger=greater
Intensity – Darker=greater
Blur – Clearer=greater
These pre-attentive attributes help us all consume and digest information and make sense of their meaning. However, Stephen points out that "pre-attentive symbols become less distinct as the variety of distracters increases. It is easy to spot a single hawk in a sky full of pigeons, but if the sky contains a greater variety of birds, the hawk will be more difficult to see."
Good visualizations not only take advantage of the pre-attentive attributes mentioned above, but they also use them appropriately while considering the limitations of our visual memory.
Stephen explains that we have both "working memory and long-term memory. Working memory stores information only briefly. Working memory is where information resides when we are thinking about it. If we think about it long enough, it will end up in long-term memory."
And visual memory (which is part of working memory) is very limited. Visual memory processes information in “chunks.” How much is a chunk of information? Well, it depends on how it is conveyed. Information chunks have to be relatively small when they consist of text or numbers. However, they can be larger when served up graphically.
Part II, which is the meat of the book, takes the reader through several different analyses and shows examples of good visual techniques to best convey the information used in each one.
Part III covers promising new trends. Here, Stephen discusses “Illuminating Predictive Models,” and this is where JMP is prominently featured.
Noting that his book largely focuses on analysis of existing information, or descriptive statistics, here he highlights the benefits of predictive statistics. "If we understand the past well enough to describe it clearly and accurately, we can often build a model that we can use to predict what will likely happen as a result of particular conditions, events, or decisions in the future," Stephen writes.
He explains that "the goal of predictive analysis is not to produce certainty about the future, but to reduce uncertainty to a degree that enables us to make better decisions.“
He then describes what he refers to as “transparent predictive models.” And it is here where Stephen uses JMP to explain how transparent predictive models help us make better decisions with less risk. He says, “This level of involvement in the analytical process [using transparent predictive models] takes advantage of our brains in a way that throws open the windows to insights that we might never otherwise experience.”
Many of the visualizations discussed in Stephen’s book are available in JMP. But even more importantly, JMP’s visualizations also incorporate world-class analytics. This is why, when Stephen turns his attention to more advanced analytics, like predictive modeling, JMP is prominently featured.
“Now You See It” is a must-read for anyone who needs to explore and understand data that guides his or her decision-making process. Following Stephen’s advice will help readers explore their data better, discover trends and patterns more quickly, and make decisions with confidence.
Stephen lays the foundation for good visual analysis in Part I by defining how we perceive information. He states, "…there are ways to visually display data that are effective because they correspond naturally to the working of vision and cognition, and there are ways that break the rules and consequently don’t work. If we wish to display information in a way that will enable us and others to make sense of it, we must understand and follow the rules.”
He goes on to explain that we "…perceive several basic attributes of visual images pre-attentively, that is, prior to and without the need for conscious perception."
Below is a list of those pre-attentive attributes that are quantitatively perceived in and of themselves, without having values arbitrarily assigned to them:
Length – longer =greater
2-D Position – offset higher/lower, or left/right=greater
Width – Wider=greater
Size – Bigger=greater
Intensity – Darker=greater
Blur – Clearer=greater
These pre-attentive attributes help us all consume and digest information and make sense of their meaning. However, Stephen points out that "pre-attentive symbols become less distinct as the variety of distracters increases. It is easy to spot a single hawk in a sky full of pigeons, but if the sky contains a greater variety of birds, the hawk will be more difficult to see."
Good visualizations not only take advantage of the pre-attentive attributes mentioned above, but they also use them appropriately while considering the limitations of our visual memory.
Stephen explains that we have both "working memory and long-term memory. Working memory stores information only briefly. Working memory is where information resides when we are thinking about it. If we think about it long enough, it will end up in long-term memory."
And visual memory (which is part of working memory) is very limited. Visual memory processes information in “chunks.” How much is a chunk of information? Well, it depends on how it is conveyed. Information chunks have to be relatively small when they consist of text or numbers. However, they can be larger when served up graphically.
Part II, which is the meat of the book, takes the reader through several different analyses and shows examples of good visual techniques to best convey the information used in each one.
Part III covers promising new trends. Here, Stephen discusses “Illuminating Predictive Models,” and this is where JMP is prominently featured.
Noting that his book largely focuses on analysis of existing information, or descriptive statistics, here he highlights the benefits of predictive statistics. "If we understand the past well enough to describe it clearly and accurately, we can often build a model that we can use to predict what will likely happen as a result of particular conditions, events, or decisions in the future," Stephen writes.
He explains that "the goal of predictive analysis is not to produce certainty about the future, but to reduce uncertainty to a degree that enables us to make better decisions.“
He then describes what he refers to as “transparent predictive models.” And it is here where Stephen uses JMP to explain how transparent predictive models help us make better decisions with less risk. He says, “This level of involvement in the analytical process [using transparent predictive models] takes advantage of our brains in a way that throws open the windows to insights that we might never otherwise experience.”
Many of the visualizations discussed in Stephen’s book are available in JMP. But even more importantly, JMP’s visualizations also incorporate world-class analytics. This is why, when Stephen turns his attention to more advanced analytics, like predictive modeling, JMP is prominently featured.
“Now You See It” is a must-read for anyone who needs to explore and understand data that guides his or her decision-making process. Following Stephen’s advice will help readers explore their data better, discover trends and patterns more quickly, and make decisions with confidence.
Friday, May 29, 2009
JMP Tree Map in Data Visualization Report
In case you missed my Twitter update about it last week, a JMP tree map created by Daniel Arneman of UNC Energy Services was featured in a recent data visualization report by Intelligent Enterprise titled "Seeing Connections: Visualization Makes Sense of Data." The report, by Seth Grimes, is available as a free download, with registration, and it's definitely worth a read.
Arneman's tree map visualizes greenhouse gas emissions by buildings on the campus of the University of North Carolina at Chapel Hill. It is also featured in the "Visualization Image Gallery" that accompanies the report (registration isn't required to view this), along with an explanation of the value of this type of data visualization:
"Cell Size Indicates Variable Contribution: The University of North Carolina at Chapel Hill developed this treemap visualization using SAS JMP software to show that certain buildings and classes of buildings are responsible for more greenhouse gas emissions than others. Treemaps are not designed to convey numerical quantities; the intent is to show relative rankings...."
You can read more about how Arneman uses JMP to assess, monitor and help reduce greenhouse gas emissions at UNC in our story about his work. You'll see the same tree map in our image gallery (though, using different colors), and you'll also see lots of other cool visualizations by Arneman, including a series of Bubble Plots and the Profiler.
Tuesday, May 26, 2009
Our Evolving Science
Thursday, May 21, 2009
Welcome to the SAS Solar Farm, Gov. Perdue
I had the privilege to lead off a press event this morning for North Carolina Gov. Bev Perdue at the SAS Solar Farm in Cary, NC. It was a beautiful photo event on a small hill overlooking the field. Here are my remarks:
Good morning and welcome to the SAS Solar Farm. I am John Sall, co-founder of SAS Institute.
This field of solar panels was completed last December; it has 5,040 panels, generates 1 megawatt of electic power at peak and is projected to produce 1.7 million KWH per year. The panels swivel to track the sun across the sky. This solar farm will eliminate 1,600 tons of carbon emissions annually.
The solar field occupies 4.8 acres of land -- and we also use the field as a pasture for Dorper Sheep (short sheep that fit better under the panels).
Eventually, the revenues from this facility will repay our investment, but only because of the generous state and federal tax credits and NC GreenPower electric rates. Without the incentives, this solar-generating facility would not have been built.
I hope that federal legislation will be forthcoming to make alternative energy and energy conservation economic by a federal charge on fossil carbon energy sourcing; this would be the most effective, efficient and ultimately the least painful way to a sustainable energy future. Until that becomes politically viable, other measures, such as alternative energy subsidies and quantity limits will at least move us in the right direction toward sustainability and energy security.
This solar farm is one of several energy initiatives at SAS. We aim to conserve energy use as we grow more jobs here:
SAS is happy to call North Carolina home, with the state’s support for business, research and higher education, all of this enabling better jobs, better health and long-term sustainability.
North Carolina has many opportunities in alternative energy: in solar, in biofuels and in wind. Recently, the federal Department of the Interior, under Ken Salazar, made the first step to unlock leasing for offshore wind farms, and North Carolina has some of the best opportunities.
North Carolina is a home for energy research, too. We congratulate NC State University, which last year was appointed the lead institution for a smart grid NSF grant, which led to the FREEDM Systems Center (Future Renewable Electric Energy Delivery and Management).
We welcome everyone here to this new energy farm, and we look forward to our governor’s announcements on the subject of sustainability.
It is my privilege to introduce the Governor of the State of North Carolina, Bev Perdue.
Dale Carroll, Deputy Secretary of NC Department of Commerce (left); Hilda Pinnix-Ragland, Vice President of Corporate Public Affairs for Progress Energy (second from left); and NC Gov. Bev Perdue (center) join Jerry Williams of SAS (second from right) and me (right) at the SAS Solar Farm this morning. The Dorper Sheep are in the background.
Photo by Steve Muir, SAS
Good morning and welcome to the SAS Solar Farm. I am John Sall, co-founder of SAS Institute.
This field of solar panels was completed last December; it has 5,040 panels, generates 1 megawatt of electic power at peak and is projected to produce 1.7 million KWH per year. The panels swivel to track the sun across the sky. This solar farm will eliminate 1,600 tons of carbon emissions annually.
The solar field occupies 4.8 acres of land -- and we also use the field as a pasture for Dorper Sheep (short sheep that fit better under the panels).
Eventually, the revenues from this facility will repay our investment, but only because of the generous state and federal tax credits and NC GreenPower electric rates. Without the incentives, this solar-generating facility would not have been built.
I hope that federal legislation will be forthcoming to make alternative energy and energy conservation economic by a federal charge on fossil carbon energy sourcing; this would be the most effective, efficient and ultimately the least painful way to a sustainable energy future. Until that becomes politically viable, other measures, such as alternative energy subsidies and quantity limits will at least move us in the right direction toward sustainability and energy security.
This solar farm is one of several energy initiatives at SAS. We aim to conserve energy use as we grow more jobs here:
- Our next building, under construction over there behind those trees, is designed to achieve LEED certification at least at the Silver level.
- Elsewhere, our forthcoming cloud computing facility will use energy-efficient computer systems.
- We recently published our second annual corporate sustainability and social responsibility report, which summarizes these and other efforts.
SAS is happy to call North Carolina home, with the state’s support for business, research and higher education, all of this enabling better jobs, better health and long-term sustainability.
North Carolina has many opportunities in alternative energy: in solar, in biofuels and in wind. Recently, the federal Department of the Interior, under Ken Salazar, made the first step to unlock leasing for offshore wind farms, and North Carolina has some of the best opportunities.
North Carolina is a home for energy research, too. We congratulate NC State University, which last year was appointed the lead institution for a smart grid NSF grant, which led to the FREEDM Systems Center (Future Renewable Electric Energy Delivery and Management).
We welcome everyone here to this new energy farm, and we look forward to our governor’s announcements on the subject of sustainability.
It is my privilege to introduce the Governor of the State of North Carolina, Bev Perdue.
Dale Carroll, Deputy Secretary of NC Department of Commerce (left); Hilda Pinnix-Ragland, Vice President of Corporate Public Affairs for Progress Energy (second from left); and NC Gov. Bev Perdue (center) join Jerry Williams of SAS (second from right) and me (right) at the SAS Solar Farm this morning. The Dorper Sheep are in the background.
Photo by Steve Muir, SAS
Wednesday, May 20, 2009
Scientific Computing Review: 'Stunning' Graphics in JMP 8
Tuesday, May 19, 2009
3-D Pie Reply
Monday, May 18, 2009
Soccer Analytics Using JMP
NOTE: This entry comes to the JMP Blog from our colleague Jerome Bryssinck of SAS Belgium. Jerome had seen Jeff Perkinson's examples of basketball analytics using JMP and created his own example using football (or soccer) data. In response to comments from readers, Jerome updated his model on May 26, and this blog post now reflects those changes.
THE QUESTION: Has the game been decided yet? HTGBD
This is the question that most people constantly ask themselves when they are watching a football game. This question can take different forms depending on the circumstances. If you're lucky to support the winning team, you might ask yourself: "How secure is the lead?" And for the less fortunate of us: "Is there still a chance for my team to win?"
THE ANSWER: Analytics
Graph1: Probability of the game having been decided in function of the elapsed time and the number of goals difference.
Graph1 shows the probablility of the game having been decided in function of the elapsed time and the number of goals difference. It is possible to change the elapsed time and the number of goal difference on the graph by clicking on a different value.
Some interpretation examples:
If Time=45 and Goal Difference=0: The game has been going on for 45 minutes, and the number of goal difference is 0. There is a 23% probability that the outcome of the game won't change. Here, as the teams are even (0 goal difference), this would mean that there is a 23% probability the game will end in a tie.
If Time=45 and Goal Difference=1: The game has been going on for 45 minutes, and one of the teams is leading by 1 goal difference, then we have a 60% probability that the outcome of the game won't change. Here, this would mean that the leading team has a 60% probability to win.
More Details about the Answer
The model used above has been built using data from the UK Premier League from 2002 to 2006. The type of model used is a regression model.
The following representations are useful to understand the underlying data.
Graph2: Has the Game Been Decided vs. Time
Graph2 shows the percentage of the games that have been decided in function of the Elapsed Time. I must say that I wasn't surprised by this graph, which basically states that the Elapsed Time and the HTGBD (Has The Game Been Decided) are directly proportional.
Graph3: Has the Game Been Decided vs. Time By Goal Difference
Graph3 shows the percentage of the games that have been decided in function of the Elapsed Time by the number of goal difference. According to this graph, the number of goal difference is an excellent predictor for the HTGBD.
Additional readings:
Similar models are available for basketball. Check out Bill James and Jeff Perkinson if you want to learn more.
This entry was first published in Jerome Bryssinck's blog, Brisink. It is republished here with his permission.
THE QUESTION: Has the game been decided yet? HTGBD
This is the question that most people constantly ask themselves when they are watching a football game. This question can take different forms depending on the circumstances. If you're lucky to support the winning team, you might ask yourself: "How secure is the lead?" And for the less fortunate of us: "Is there still a chance for my team to win?"
THE ANSWER: Analytics
Graph1: Probability of the game having been decided in function of the elapsed time and the number of goals difference.
Graph1 shows the probablility of the game having been decided in function of the elapsed time and the number of goals difference. It is possible to change the elapsed time and the number of goal difference on the graph by clicking on a different value.
Some interpretation examples:
If Time=45 and Goal Difference=0: The game has been going on for 45 minutes, and the number of goal difference is 0. There is a 23% probability that the outcome of the game won't change. Here, as the teams are even (0 goal difference), this would mean that there is a 23% probability the game will end in a tie.
If Time=45 and Goal Difference=1: The game has been going on for 45 minutes, and one of the teams is leading by 1 goal difference, then we have a 60% probability that the outcome of the game won't change. Here, this would mean that the leading team has a 60% probability to win.
More Details about the Answer
The model used above has been built using data from the UK Premier League from 2002 to 2006. The type of model used is a regression model.
The following representations are useful to understand the underlying data.
Graph2: Has the Game Been Decided vs. Time
Graph2 shows the percentage of the games that have been decided in function of the Elapsed Time. I must say that I wasn't surprised by this graph, which basically states that the Elapsed Time and the HTGBD (Has The Game Been Decided) are directly proportional.
Graph3: Has the Game Been Decided vs. Time By Goal Difference
Graph3 shows the percentage of the games that have been decided in function of the Elapsed Time by the number of goal difference. According to this graph, the number of goal difference is an excellent predictor for the HTGBD.
Additional readings:
Similar models are available for basketball. Check out Bill James and Jeff Perkinson if you want to learn more.
This entry was first published in Jerome Bryssinck's blog, Brisink. It is republished here with his permission.
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