Interview Anne Milley JMP

Here is an interview with Anne Milley,Sr Director, Analytic Strategy, JMP.

Ajay- Review – How was the year 2012 for Analytics in general and JMP in particular?
Anne- 2012 was great!  Growing interest in analytics is evident—more analytics books, blogs, LinkedIn groups, conferences, training, capability, integration….  JMP had another good year of worldwide double-digit growth.

Ajay-  Forecast- What is your forecast for analytics in terms of top 5 paradigms for 2013?
Anne- In an earlier blog, I had predicted we will continue to see more lively data and information visualizations—by that I mean more interactive and dynamic graphics for both data analysts and information consumers.
We will continue to hear about big data, data science and other trendy terms. As we amass more and more data histories, we can expect to see more innovations in time series visualization. I am excited by the growing interest we see in spatial and image analysis/visualization and hope those trends continue—especially more objective, data-driven image analysis in medicine! Perhaps not a forecast, but a strong desire, to see more people realize and benefit from the power of experimental design. We are pleased that more companies—most recently SiSoft—have integrated with JMP to make DOE a more seamless part of the design engineer’s workflow.

 Ajay- Cloud- Cloud Computing seems to be the next computing generation. What are JMP plans for cloud computing?
Anne- With so much memory and compute power on the desktop, there is still plenty of action on PCs. That said, JMP is Citrix-certified and we do see interest in remote desktop virtualization, but we don’t support public clouds.

Ajay- Events- What are your plans for the International Year of Statistics at JMP?
Anne- We kicked off our Analytically Speaking webcast series this year with John Sall in recognition of the first-ever International Year of Statistics. We have a series of blog posts on our International Year of Statistics site that features a noteworthy statistician each month, and in keeping with the goals of Statistics2013, we are happy to:

  • increase awareness of statistics and why it’s essential,
  • encourage people to consider it as a profession and/or enhance their skills with more statistical knowledge, and
  • promote innovation in the sciences of probability and statistics.

Both JMP and SAS are doing a variety of other things to help celebrate statistics all year long!

Ajay- Education Training-  How does JMP plan to leverage the MOOC paradigm (massive open online course) as offered by providers like Coursera etc.?
Anne- Thanks to you for posting this to the JMP Professional Network  on LinkedIn, where there is some great discussion on this topic.  The MOOC concept is wonderful—offering people the ability to invest in themselves, enhance their understanding on such a wide variety of topics, improve their communities….  Since more and more professors are teaching with JMP, it would be great to see courses on various areas of statistics (especially since this is the International Year of Statistics!) using JMP. JMP strives to remove complexity and drudgery from the analysis process so the analyst can stay in flow and focus on solving the problem at hand. For instance, the one-click bootstrap is a great example of something that should be promoted in an intro stats class. Imagine getting to appreciate the applied results and see the effects of sampling variability without having to know distribution theory. It’s good that people have options to enhance their skills—people can download a 30-day free trial of JMP and browse our learning library as well.

Ajay- Product- What are some of the exciting things JMP users and fans can look forward to in the next releases this year?
Anne- There are a number of enhancements and new capabilities planned for new releases of the JMP family of products, but you will have to wait to hear details…. OK, I’ll share a few!  JMP Clinical 4.1 will have more sophisticated fraud detection. We are also excited about releasing version 11 of JMP and JMP Pro this September.  JMP’s DOE capability is well-known, and we are pleased to offer a brand new class of experimental design—definitive screening designs. This innovation has already been recognized with The 2012 Statistics in Chemistry Award to Scott Allen of Novomer in collaboration with Bradley Jones in the JMP division of SAS. You will hear more about the new releases of JMP and JMP Pro at  Discovery Summit in San Antonio—we are excited to have Nate Silver as our headliner!

About-

Anne Milley directs analytic strategy in JMP Product Marketing at SAS.  Her ties to SAS began with bank failure prediction at FHLB Dallas.  Using SAS continued at 7-Eleven Corporation in Strategic Planning.  She has authored papers and served on committees for SAS Education conferences, KDD, and SIAM.  In 2008, she completed a 5-month assignment at a UK bank.  Milley completed her M.A. in Economics from Florida Atlantic University, did post-graduate work at RWTH Aachen, and is proficient in German.

JMP-

Introduced in 1989, JMP has grown into a family of statistical discovery products used worldwide in almost every industry. JMP is statistical discovery software  that links dynamic data visualization with robust statistics, in memory and on the desktop. From its beginnings, JMP software has empowered its users by enabling interactive analytics on the desktop. JMP products continue to complement – and are often deployed with – analytics solutions that provide server-based business intelligence.

 

JMP Student Edition

I really liked the initiatives at JMP/Academic. Not only they offer the software bundled with a textbook, which is both good common sense as well as business sense given how fast students can get confused

(Rant 1 Bundling with textbooks is something I think is Revolution Analytics should think of doing instead of just offering the academic  version for free downloading- it would be interesting to see the penetration of R academic market with Revolution’s version and the open source version with the existing strategy)

From http://www.jmp.com/academic/textbooks.shtml

Major publishers of introductory statistics textbooks offer a 12-month license to JMP Student Edition, a streamlined version of JMP, with their textbooks.

and a glance through this http://www.jmp.com/academic/pdf/jmp_se_comparison.pdf  shows it is a credible and not extremely whittled down version which would be just dishonest.

And I loved this Reference Card at http://www.jmp.com/academic/pdf/jmp10_se_quick_guide.pdf

 

Oracle, SAP- Hana, Revolution Analytics and even SAS/STAT itself can make more reference cards like this- elegant solutions for students and new learners!

More- creative-rants Honestly why do corporate sites use PDFs anymore when they can use Instapaper , or any of these SlideShare/Scribd formats to show information in a better way without diverting the user from the main webpage.

But I digress, back to JMP

 

Resources for Faculty Using JMP® Student Edition

Faculty who select a JMP Student Edition bundle for their courses may be eligible for additional resources, including course materials and training.

Special JMP® Student Edition for AP Statistics

JMP Student Edition is available in a convenient five-year license for qualified Advanced Placement statistics programs.

Try and have a look yourself at http://www.jmp.com/academic/student.shtml

 

 

 

JMP 10 released

JMP , the visual data exploration, statistical quality control software from SAS Institute launched version 10 of its software today.

Source-http://jmp.com/about/events/webcasts/jmp_webcast.shtml?name=jmp10

JMP 10 includes:

Numerous enhancements to the drag-and-drop Graph Builder, including a new iPad application.

A cutting-edge Control Chart Builder to create process control charts with drag-and-drop ease.

New reliability capabilities, including growth and forecast models.

Additions and improvements for sorting and filtering data, design of experiments, statistical modeling, scripting, add-in and application development, script debugging and more.

From JohnSall’s blog post at http://blogs.sas.com/content/jmp/2012/03/20/discover-more-with-jmp-10/

Much of the development centered on four focus areas:

1. Graph Builder everywhere. The Graph Builder platform itself has new features like Heatmap and Treemap, an elements palette and properties panel, making the choices more visible. But Graph Builder also has some descendents now, including the new Control Chart Builder, which makes creating control charts an interactive process. In addition, some of the drag-and-drop features that are used to change columns in Graph Builder are also available in Distribution, Fit Y by X, and a few other places. Finally, Graph Builder has been ported to the iPad. For the first time, you can use JMP for exploration and presentation on a mobile device for free. So just think of Graph Builder as gradually taking over in lots of places.

2. Expert-driven design.reliability, measurement systems, and partial least squares analyses.

3. Performance.  this release has the most new multithreading so far

4. Application development

You can read more here –http://jmp.com/about/events/webcasts/jmpwebcast_detail.shtml?reglink=70130000001r9IP

Interview Kelci Miclaus, SAS Institute Using #rstats with JMP

Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.

 

Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?

Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.

One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.

I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.

(see http://www.decisionstats.com/jmp-and-r-rstats/)

Ajay- Are there plans to extend JMP integration with other languages like Python?

Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.

 Ajay- How is R a complimentary fit to JMP’s technical capabilities?

Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.

JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.

Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.

Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?

Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.

Ajay-  While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.

 Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included.  Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.

We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.

About- Kelci Miclaus

Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.

.

 

JMP and R – #rstats

An amazing example of R being used sucessfully in combination (and not is isolation) with other enterprise software is the add-ins functionality of JMP and it’s R integration.

See the following JMP add-ins which use R

http://support.sas.com/demosdownloads/downarea_t4.jsp?productID=110454&jmpflag=Y

JMP Add-in: Multidimensional Scaling using R

This add-in creates a new menu command under the Add-Ins Menu in the submenu R Add-ins. The script will launch a custom dialog (or prompt for a JMP data table is one is not already open) where you can cast columns into roles for performing MDS on the data table. The analysis results in a data table of MDS dimensions and associated output graphics. MDS is a dimension reduction method that produces coordinates in Euclidean space (usually 2D, 3D) that best represent the structure of a full distance/dissimilarity matrix. MDS requires that input be a symmetric dissimilarity matrix. Input to this application can be data that is already in the form of a symmetric dissimilarity matrix or the dissimilarity matrix can be computed based on the input data (where dissimilarity measures are calculated between rows of the input data table in R).

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis

Chernoff Faces Add-in

One way to plot multivariate data is to use Chernoff faces. For each observation in your data table, a face is drawn such that each variable in your data set is represented by a feature in the face. This add-in uses JMP’s R integration functionality to create Chernoff faces. An R install and the TeachingDemos R package are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Data Visualization

Support Vector Machine for Classification

By simply opening a data table, specifying X, Y variables, selecting a kernel function, and specifying its parameters on the user-friendly dialog, you can build a classification model using Support Vector Machine. Please note that R package ‘e1071’ should be installed before running this dialog. The package can be found from http://cran.r-project.org/web/packages/e1071/index.html.

Submitted by: Jong-Seok Lee SAS employee Initiative: All
Application: Add-Ins Analysis: Exploratory Data Analysis/Mining

Penalized Regression Add-in

This add-in uses JMP’s R integration functionality to provide access to several penalized regression methods. Methods included are the LASSO (least absolutee shrinkage and selection operator, LARS (least angle regression), Forward Stagewise, and the Elastic Net. An R install and the “lars” and “elasticnet” R packages are required to use this add-in.

Submitted by: Clay Barker SAS employee Initiative: All
Application: Add-Ins Analysis: Regression

MP Addin: Univariate Nonparametric Bootstrapping

This script performs simple univariate, nonparametric bootstrap sampling by using the JMP to R Project integration. A JMP Dialog is built by the script where the variable you wish to perform bootstrapping over can be specified. A statistic to compute for each bootstrap sample is chosen and the data are sent to R using new JSL functionality available in JMP 9. The boot package in R is used to call the boot() function and the boot.ci() function to calculate the sample statistic for each bootstrap sample and the basic bootstrap confidence interval. The results are brought back to JMP and displayed using the JMP Distribution platform.

Submitted by: Kelci Miclaus SAS employee Initiative: All
Application: Add-Ins Analysis: Basic Statistics

#SAS 9.3 and #Rstats 2.13.1 Released

A bit early but the latest editions of both SAS and R were released last week.

SAS 9.3 is clearly a major release with multiple enhancements to make SAS both relevant and pertinent in enterprise software in the age of big data. Also many more R specific, JMP specific and partners like Teradata specific enhancements.

http://support.sas.com/software/93/index.html

Features

Data management

  • Enhanced manageability for improved performance
  • In-database processing (EL-T pushdown)
  • Enhanced performance for loading oracle data
  • New ET-L transforms
  • Data access

Data quality

  • SAS® Data Integration Server includes DataFlux® Data Management Platform for enhanced data quality
  • Master Data Management (DataFlux® qMDM)
    • Provides support for master hub of trusted entity data.

Analytics

  • SAS® Enterprise Miner™
    • New survival analysis predicts when an event will happen, not just if it will happen.
    • New rate making capability for insurance predicts optimal insurance premium for individuals based on attributes known at application time.
    • Time Series Data Mining node (experimental) applies data mining techniques to transactional, time-stamped data.
    • Support Vector Machines node (experimental) provides a supervised machine learning method for prediction and classification.
  • SAS® Forecast Server
    • SAS Forecast Server is integrated with the SAP APO Demand Planning module to provide SAP users with access to a superior forecasting engine and automatic forecasting capabilities.
  • SAS® Model Manager
    • Seamless integration of R models with the ability to register and manage R models in SAS Model Manager.
    • Ability to perform champion/challenger side-by-side comparisons between SAS and R models to see which model performs best for a specific need.
  • SAS/OR® and SAS® Simulation Studio
    • Optimization
    • Simulation
      • Automatic input distribution fitting using JMP with SAS Simulation Studio.

Text analytics

  • SAS® Text Miner
  • SAS® Enterprise Content Categorization
  • SAS® Sentiment Analysis

Scalability and high-performance

  • SAS® Analytics Accelerator for Teradata (new product)
  • SAS® Grid Manager
 and latest from http://www.r-project.org/ I was a bit curious to know why the different licensing for R now (from GPL2 to GPL2- GPL 3)

LICENCE:

No parts of R are now licensed solely under GPL-2. The licences for packages rpart and survival have been changed, which means that the licence terms for R as distributed are GPL-2 | GPL-3.


This is a maintenance release to consolidate various minor fixes to 2.13.0.
CHANGES IN R VERSION 2.13.1:

  NEW FEATURES:

    • iconv() no longer translates NA strings as "NA".

    • persp(box = TRUE) now warns if the surface extends outside the
      box (since occlusion for the box and axes is computed assuming
      the box is a bounding box). (PR#202.)

    • RShowDoc() can now display the licences shipped with R, e.g.
      RShowDoc("GPL-3").

    • New wrapper function showNonASCIIfile() in package tools.

    • nobs() now has a "mle" method in package stats4.

    • trace() now deals correctly with S4 reference classes and
      corresponding reference methods (e.g., $trace()) have been added.

    • xz has been updated to 5.0.3 (very minor bugfix release).

    • tools::compactPDF() gets more compression (usually a little,
      sometimes a lot) by using the compressed object streams of PDF
      1.5.

    • cairo_ps(onefile = TRUE) generates encapsulated EPS on platforms
      with cairo >= 1.6.

    • Binary reads (e.g. by readChar() and readBin()) are now supported
      on clipboard connections.  (Wish of PR#14593.)

    • as.POSIXlt.factor() now passes ... to the character method
      (suggestion of Joshua Ulrich).  [Intended for R 2.13.0 but
      accidentally removed before release.]

    • vector() and its wrappers such as integer() and double() now warn
      if called with a length argument of more than one element.  This
      helps track down user errors such as calling double(x) instead of
      as.double(x).

  INSTALLATION:

    • Building the vignette PDFs in packages grid and utils is now part
      of running make from an SVN checkout on a Unix-alike: a separate
      make vignettes step is no longer required.

      These vignettes are now made with keep.source = TRUE and hence
      will be laid out differently.

    • make install-strip failed under some configuration options.

    • Packages can customize non-standard installation of compiled code
      via a src/install.libs.R script. This allows packages that have
      architecture-specific binaries (beyond the package's shared
      objects/DLLs) to be installed in a multi-architecture setting.

  SWEAVE & VIGNETTES:

    • Sweave() and Stangle() gain an encoding argument to specify the
      encoding of the vignette sources if the latter do not contain a
      \usepackage[]{inputenc} statement specifying a single input
      encoding.

    • There is a new Sweave option figs.only = TRUE to run each figure
      chunk only for each selected graphics device, and not first using
      the default graphics device.  This will become the default in R
      2.14.0.

    • Sweave custom graphics devices can have a custom function
      foo.off() to shut them down.

    • Warnings are issued when non-portable filenames are found for
      graphics files (and chunks if split = TRUE).  Portable names are
      regarded as alphanumeric plus hyphen, underscore, plus and hash
      (periods cause problems with recognizing file extensions).

    • The Rtangle() driver has a new option show.line.nos which is by
      default false; if true it annotates code chunks with a comment
      giving the line number of the first line in the sources (the
      behaviour of R >= 2.12.0).

    • Package installation tangles the vignette sources: this step now
      converts the vignette sources from the vignette/package encoding
      to the current encoding, and records the encoding (if not ASCII)
      in a comment line at the top of the installed .R file.

  DEPRECATED AND DEFUNCT:

    • The internal functions .readRDS() and .saveRDS() are now
      deprecated in favour of the public functions readRDS() and
      saveRDS() introduced in R 2.13.0.

    • Switching off lazy-loading of code _via_ the LazyLoad field of
      the DESCRIPTION file is now deprecated.  In future all packages
      will be lazy-loaded.

    • The off-line help() types "postscript" and "ps" are deprecated.

  UTILITIES:

    • R CMD check on a multi-architecture installation now skips the
      user's .Renviron file for the architecture-specific tests (which
      do read the architecture-specific Renviron.site files).  This is
      consistent with single-architecture checks, which use
      --no-environ.

    • R CMD build now looks for DESCRIPTION fields BuildResaveData and
      BuildKeepEmpty for per-package overrides.  See ‘Writing R
      Extensions’.

  BUG FIXES:

    • plot.lm(which = 5) was intended to order factor levels in
      increasing order of mean standardized residual.  It ordered the
      factor labels correctly, but could plot the wrong group of
      residuals against the label.  (PR#14545)

    • mosaicplot() could clip the factor labels, and could overlap them
      with the cells if a non-default value of cex.axis was used.
      (Related to PR#14550.)

    • dataframe[[row,col]] now dispatches on [[ methods for the
      selected column (spotted by Bill Dunlap).

    • sort.int() would strip the class of an object, but leave its
      object bit set.  (Reported by Bill Dunlap.)

    • pbirthday() and qbirthday() did not implement the algorithm
      exactly as given in their reference and so were unnecessarily
      inaccurate.

      pbirthday() now solves the approximate formula analytically
      rather than using uniroot() on a discontinuous function.

      The description of the problem was inaccurate: the probability is
      a tail probablity (‘2 _or more_ people share a birthday’)

    • Complex arithmetic sometimes warned incorrectly about producing
      NAs when there were NaNs in the input.

    • seek(origin = "current") incorrectly reported it was not
      implemented for a gzfile() connection.

    • c(), unlist(), cbind() and rbind() could silently overflow the
      maximum vector length and cause a segfault.  (PR#14571)

    • The fonts argument to X11(type = "Xlib") was being ignored.

    • Reading (e.g. with readBin()) from a raw connection was not
      advancing the pointer, so successive reads would read the same
      value.  (Spotted by Bill Dunlap.)

    • Parsed text containing embedded newlines was printed incorrectly
      by as.character.srcref().  (Reported by Hadley Wickham.)

    • decompose() used with a series of a non-integer number of periods
      returned a seasonal component shorter than the original series.
      (Reported by Rob Hyndman.)

    • fields = list() failed for setRefClass().  (Reported by Michael
      Lawrence.)

    • Reference classes could not redefine an inherited field which had
      class "ANY". (Reported by Janko Thyson.)

    • Methods that override previously loaded versions will now be
      installed and called.  (Reported by Iago Mosqueira.)

    • addmargins() called numeric(apos) rather than
      numeric(length(apos)).

    • The HTML help search sometimes produced bad links.  (PR#14608)

    • Command completion will no longer be broken if tail.default() is
      redefined by the user. (Problem reported by Henrik Bengtsson.)

    • LaTeX rendering of markup in titles of help pages has been
      improved; in particular, \eqn{} may be used there.

    • isClass() used its own namespace as the default of the where
      argument inadvertently.

    • Rd conversion to latex mis-handled multi-line titles (including
      cases where there was a blank line in the \title section).
Also see this interesting blog
Examples of tasks replicated in SAS and R

East loves Gold and USD. and chokes on it

A brief analysis shows how Eastern Hemisphere loves gold and USD so much

I did the graph in JMP since it is an easier GUI for me to use (I do have some learning disabilities).

https://www.cia.gov/library/publications/the-world-factbook/rankorder/2188rank.html

RANK
COUNTRY RESERVES OF FOREIGN EXCHANGE AND GOLD DATE OF INFORMATION
1 China
$ 2,622,000,000,000
31 December 2010 est.
2 Japan
$ 1,096,000,000,000
31 December 2010 est.
3 Russia
$ 483,100,000,000
30 November 2010
4 Saudi Arabia
$ 456,200,000,000
31 December 2010 est.
5 Taiwan
$ 387,200,000,000
31 December 2010 est.
6 Brazil
$ 290,900,000,000
31 December 2010 est.
7 India
$ 284,100,000,000
31 December 2010 est.
8 Korea, South
$ 274,600,000,000
31 December 2010 est.
9 Hong Kong
$ 268,900,000,000
31 December 2010 est.
10 Switzerland
$ 236,600,000,000
31 December 2010
11 Singapore
$ 225,800,000,000
31 December 2010 est.
12 Thailand
$ 176,100,000,000
31 December 2010 est.
13 Algeria
$ 150,100,000,000
31 December 2010 est.
14 Mexico
$ 116,400,000,000
31 December 2010 est.
15 Libya
$ 107,300,000,000
31 December 2010 est.
16 Malaysia
$ 106,500,000,000
31 December 2010 est.
17 Poland
$ 99,760,000,000
31 December 2010 est.
18 Indonesia
$ 96,210,000,000
31 December 2010 est.
19 Turkey
$ 78,000,000,000
31 December 2010 est.
20 Iran
$ 75,060,000,000
31 December 2010 est.
21 Israel
$ 66,980,000,000
31 December 2010 est.
22 Philippines
$ 62,370,000,000
31 December 2010 est.
23 Argentina
$ 53,610,000,000
31 December 2010 est.
24 Romania
$ 50,510,000,000
31 December 2010 est.
25 Iraq
$ 45,680,000,000
31 December 2010 est.
26 South Africa
$ 45,520,000,000
31 December 2010 est.
27 Hungary
$ 44,990,000,000
31 December 2010 est.
28 Peru
$ 44,110,000,000
31 December 2010
29 Nigeria
$ 43,360,000,000
31 December 2010 est.
30 Czech Republic
$ 42,340,000,000
31 December 2010 est.
31 Lebanon
$ 41,570,000,000
31 December 2010 est.
32 United Arab Emirates
$ 39,100,000,000
31 December 2010 est.
33 Australia
$ 38,620,000,000
31 December 2010 est.
34 Egypt
$ 35,720,000,000
31 December 2010 est.
35 Ukraine
$ 32,910,000,000
31 December 2010 est.
36 Kazakhstan
$ 32,440,000,000
31 December 2010 est.
37 Venezuela
$ 29,490,000,000
31 December 2010 est.
38 Colombia
$ 28,500,000,000
31 December 2010 est.
39 Chile
$ 26,080,000,000
31 December 2010 est.
40 Morocco
$ 24,570,000,000
31 December 2010 est.
41 Macau
$ 23,730,000,000
42 Kuwait
$ 22,420,000,000
31 December 2010 est.
43 Qatar
$ 22,410,000,000
31 December 2010 est.
44 Austria
$ 21,890,000,000
31 December 2010 est.
45 Syria
$ 17,960,000,000
31 December 2010 est.
46 New Zealand
$ 17,850,000,000
31 December 2010 est.
47 Bulgaria
$ 17,270,000,000
31 December 2010 est.
48 Angola
$ 16,890,000,000
31 December 2010 est.
49 Pakistan
$ 16,100,000,000
31 December 2010 est.
50 Serbia
$ 15,100,000,000
30 November 2010 est.
51 Oman
$ 14,000,000,000
31 December 2010 est.
52 Croatia
$ 13,790,000,000
31 December 2010 est.
53 Vietnam
$ 13,000,000,000
31 December 2010 est.
54 Jordan
$ 12,640,000,000
31 December 2010 est.
55 Tunisia
$ 11,230,000,000
31 December 2010 est.
56 Turkmenistan
$ 10,810,000,000
31 December 2010 est.
57 Bangladesh
$ 10,790,000,000
31 December 2010 est.
58 Uzbekistan
$ 10,500,000,000
31 December 2010 est.
59 Bolivia
$ 9,730,000,000
31 December 2010 est.
60 Trinidad and Tobago
$ 9,659,000,000
31 December 2010 est.
61 Finland
$ 9,128,000,000
31 December 2010 est.
62 Botswana
$ 7,834,000,000
31 December 2010 est.
63 Uruguay
$ 7,700,000,000
31 December 2010 est.
64 Latvia
$ 7,170,000,000
31 December 2010 est.
65 Lithuania
$ 6,418,000,000
31 December 2010 est.
66 Azerbaijan
$ 6,330,000,000
31 December 2010 est.
67 Belarus
$ 5,755,000,000
31 December 2010 est.
68 Yemen
$ 5,744,000,000
31 December 2010 est.
69 Guatemala
$ 5,709,000,000
31 December 2010 est.
70 Sri Lanka
$ 5,630,000,000
31 December 2010 est.
71 Cuba
$ 4,847,000,000
31 December 2010 est.
72 Kenya
$ 4,585,000,000
31 December 2010 est.
73 Costa Rica
$ 4,584,000,000
31 December 2010 est.
74 Iceland
$ 4,206,000,000
31 December 2010 est.
75 Bosnia and Herzegovina
$ 4,200,000,000
31 December 2010 est.
76 Paraguay
$ 4,130,000,000
31 December 2010 est.
77 Congo, Republic of the
$ 4,123,000,000
31 December 2010 est.
78 Equatorial Guinea
$ 4,086,000,000
31 December 2010 est.
79 Cameroon
$ 4,023,000,000
31 December 2010 est.
80 Cote d’Ivoire
$ 3,985,000,000
31 December 2010 est.
81 Cambodia
$ 3,840,000,000
31 December 2010 est.
82 Ghana
$ 3,800,000,000
31 December 2010 est.
83 Bahrain
$ 3,766,000,000
31 December 2010 est.
84 Burma
$ 3,762,000,000
31 December 2010 est.
85 Uganda
$ 3,743,000,000
31 December 2010 est.
86 Tanzania
$ 3,687,000,000
31 December 2010 est.
87 Estonia
$ 3,641,000,000
31 December 2010 est.
88 Ecuador
$ 3,590,000,000
31 December 2010 est.
89 Panama
$ 3,525,000,000
31 December 2010 est.
90 Papua New Guinea
$ 3,017,000,000
31 December 2010 est.
91 El Salvador
$ 2,882,000,000
31 December 2010 est.
92 Dominican Republic
$ 2,705,000,000
31 December 2010 est.
93 Gabon
$ 2,602,000,000
31 December 2010 est.
94 Mauritius
$ 2,360,000,000
31 December 2010 est.
95 Georgia
$ 2,350,000,000
31 December 2010 est.
96 Honduras
$ 2,302,000,000
31 December 2010 est.
97 Zambia
$ 2,287,000,000
31 December 2010 est.
98 Armenia
$ 2,247,000,000
31 December 2010 est.
99 Macedonia
$ 2,217,000,000
30 November 2010 est.
100 Senegal
$ 2,200,000,000
31 December 2010 est.
101 Ireland
$ 2,104,000,000
31 December 2010
102 Sudan
$ 2,063,000,000
31 December 2010 est.
103 Albania
$ 1,992,000,000
31 December 2010 est.
104 Mozambique
$ 1,982,000,000
31 December 2010 est.
105 Namibia
$ 1,961,000,000
31 December 2010 est.
106 Ethiopia
$ 1,880,000,000
31 December 2010 est.
107 Jamaica
$ 1,850,000,000
31 December 2010 est.
108 Moldova
$ 1,710,000,000
31 December 2010 est.
109 Kyrgyzstan
$ 1,615,000,000
31 December 2010 est.
110 Burkina Faso
$ 1,588,000,000
31 December 2010 est.
111 Haiti
$ 1,587,000,000
31 December 2010 est.
112 Nicaragua
$ 1,580,000,000
31 December 2010 est.
113 Benin
$ 1,254,000,000
31 December 2010 est.
114 Slovakia
$ 1,160,000,000
31 January 2010 est.
115 Madagascar
$ 1,038,000,000
31 December 2010 est.
116 Congo, Democratic Republic of the
$ 1,010,000,000
March 2010 est.
117 Lesotho
$ 893,000,000
31 December 2010 est.
118 Chad
$ 868,000,000
31 December 2010 est.
119 Rwanda
$ 816,000,000
31 December 2010 est.
120 Laos
$ 756,000,000
31 December 2010 est.
121 Swaziland
$ 708,000,000
31 December 2010 est.
122 Togo
$ 686,000,000
31 December 2010 est.
123 Barbados
$ 620,000,000
2007
124 Malta
$ 522,000,000
31 December 2010 est.
125 Guyana
$ 506,000,000
31 December 2010 est.
126 Zimbabwe
$ 376,000,000
31 December 2010 est.
127 Burundi
$ 320,000,000
31 December 2010 est.
128 Tajikistan
$ 303,000,000
31 December 2010 est.
129 Malawi
$ 301,000,000
31 December 2010 est.
130 Cape Verde
$ 296,000,000
31 December 2010 est.
131 Suriname
$ 263,300,000
2006
132 Belize
$ 219,000,000
31 December 2010 est.
133 Gambia, The
$ 203,000,000
31 December 2010 est.
134 Seychelles
$ 193,000,000
31 December 2010 est.
135 Eritrea
$ 104,000,000
31 December 2010 est.
136 Samoa
$ 70,150,000
FY03/04
137 Sao Tome and Principe
$ 46,000,000
31 December 2010 est.
138 Tonga
$ 40,830,000
FY04/05
139 Vanuatu
$ 40,540,000
2003