64-Bit JMP for Linux
Besides JMP, the only statistical software available on the Linux desktop today is an open-source product called R. It has considerable analytical depth and its open-source nature allows statisticians who also have computer programming talent to extend R. However, for the vast majority of people working in statistics today, JMP is generally acknowledged to be a better choice. JMP has an intuitive graphical user interface, a broad range of deep analytical capabilities and comprehensive professionally written documentation. JMP customers have the confidence that their investment is backed by SAS's award-winning, PhD-staffed quality assurance, technical support and professional training and consulting services. SAS boasts a 30-year record of continuous growth, so JMP customers know they can count on SAS and JMP to be around for the long haul.
“People keep wondering if Linux will ever be a serious contender in the desktop market”, says Potter. “It's been disappointing to Linux enthusiasts that this hasn't yet happened. Now, with the availability of affordable 64-bit desktop machines, we might start to see that change”, Potter says. He continues:
From the server perspective, the Linux operating system is generally recognized to be more reliable and secure at a lower cost of ownership than the alternatives.
Many research scientists and engineers would have liked to adopt Linux on their desktops too. They have refrained from doing so, however, because the applications they depended upon and the computing power they needed simply weren't there. Now those obstacles are gone.
As more of the applications that researchers depend upon, like JMP, become available for 64-bit Linux, its share of the desktop market can only grow.
Erin Vang, International Program Manager for JMP R&D at SAS, built JMP's localization and internationalization program. Previously, she was documentation and localization manager for Abacus Concepts (StatView) and technical writer and quality assurance manager for SYSTAT. She holds a B.Mus. in music performance, music history and math from St. Olaf College and an M.Mus. in horn performance from Northwestern University.
|Non-Linux FOSS: libnotify, OS X Style||Jun 18, 2013|
|Containers—Not Virtual Machines—Are the Future Cloud||Jun 17, 2013|
|Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer||Jun 12, 2013|
|Weechat, Irssi's Little Brother||Jun 11, 2013|
|One Tail Just Isn't Enough||Jun 07, 2013|
|Introduction to MapReduce with Hadoop on Linux||Jun 05, 2013|
- Containers—Not Virtual Machines—Are the Future Cloud
- Non-Linux FOSS: libnotify, OS X Style
- Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer
- Linux Systems Administrator
- RSS Feeds
- Introduction to MapReduce with Hadoop on Linux
- Validate an E-Mail Address with PHP, the Right Way
- Weechat, Irssi's Little Brother
- Tech Tip: Really Simple HTTP Server with Python
- New Products
- Poul-Henning Kamp: welcome to
1 hour 24 min ago
- This has already been done
1 hour 25 min ago
- Reply to comment | Linux Journal
2 hours 10 min ago
- Welcome to 1998
2 hours 59 min ago
- notifier shortcomings
3 hours 22 min ago
4 hours 59 min ago
- Android User
5 hours 1 min ago
- Reply to comment | Linux Journal
6 hours 54 min ago
9 hours 43 min ago
- This is a good post. This
14 hours 56 min ago
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?