Sony finalizes divorce with Ericsson, renames itself Sony Mobile Communications

16 Feb

 

 

 

More than half of America’s married couples will tell you, breaking up is hard. Hard and expensive. After living in denial, dodging rumors and eventually coming to terms with the inevitable, Sony has finally taken over Telefonaktiebolaget LM Ericsson’s 50-percent stake in the pair’s former joint venture, a move that was earlier reported to have cost €1.05 billion ($1.37 billion) to complete. The now fully Sony owned Sony Ericsson will be renamed Sony Mobile Communications, though a few of the outfit’s already announced children are keeping their papa’s name. Hit the break for Sony’s small press release.

Measurement Scales and Data Types

3 Feb

Introduction to Measurement Scales and Data Types

This tutorial discusses a classification system that is often used to describe the measurement of concepts or variables that are used in social sciences and behavioral research. This classification system categorizes the variables as being measured on either a nominal, ordinal, interval, or ratio scale. After introducing the classification system and providing examples of variables which are typically measured on each type of scale, we note the implications of these measurement scales for the analysis of data. Specifically, we discuss the statistical tests which are most appropriate for data measured on each type of scale. Finally, we will briefly consider some of the limits and criticisms of this classification system.

 

I. Nominal, Ordinal, Interval, and Ratio measurement scales

In the social and behavioral sciences, as in many other areas of science, we typically assign numbers to various attributes of people, objects, or concepts. This process is known as measurement. For example, we can measure the height of a person by assigning the person a number based on the number of inches tall that person is. Or, we can measure the size of a city by assigning the city a number which is equal to the number of residents in that city.

Sometimes the assignment of numbers to concepts we are studying is rather crude, such as when we assign a number to reflect a person’s gender (i.e., Male = 0 and Female = 1). This type of measurement is known as a Nominal measurement scale. A Nominal measurement scale is used for variables in which each participant or observation in the study must be placed into one mutually exclusive and exhaustive category. For example, categorizing study participants into “male” and “female” categories demonstrates that ‘sex’ is measured on a nominal scale. Every observation in the study falls into one, and only one, Nominal category.

With a nominal measurement scale, there is no relative ordering of the categories — the assignment of numeric scores to each category (Male, Female) is purely arbitrary. The next level of measurement, Ordinal measurement scales, do indicate something about the rank-ordering of study participants. For example, if you think of some type of competition or race (swimming, running), it is possible to rank order the finishers from first place to last place. If someone tells you they finished 2nd, you know that one person finished ahead of them, and all other participants finished behind them.

Although ordinal variables provide information concerning the relative position of participants or observations in our research study, ordinal variables do not tell us anything about the absolute magnitude of the difference between 1st and 2nd or between 2nd and 3rd. That is, we know 1st was before 2nd, and 2nd was before 3rd, but we do not know how close 3rd was to 2nd or how close 2nd was to 1st. The 1st place finisher could have been a great deal ahead of the 2nd place finisher, who finished a great deal ahead of the 3rd place finisher; or, the 1st, 2nd, and 3rd place finishers may have all finished very close together. The image below illustrates the ordinal ranking of individuals in a competition. The tick mark to the far right illustrates the person who finished in first place, while the tick mark to the far left represents the person who finished sixth out of six.

The limits of ordinal data are most apparent when one looks at the distance between the third and the fourth place finishers. Although the absolute distance between third and fourth was not that large, the measurement of ordinal data does not indicate this detail.

The next level of measurement, Interval scales, provide us with still more quantitative information. When a variable is measured on an interval scale, the distance between numbers or units on the scale is equal over all levels of the scale. An example of an Interval scale is the Farenheit scale of temperature. In the Farenheit temperature scale, the distance between 20 degrees and 40 degrees is the same as the distance between 75 degrees and 95 degrees.

With Interval scales, there is no absolute zero point. For this reason, it is inappropriate to express Interval level measurements as ratios; it would not be appropriate to say that 60 degrees is twice as hot as 30 degrees. Our final type of measurement scales, Ratio scales, do have a fixed zero point. Not only are numbers or units on the scale equal over all levels of the scale, but there is also a meaningful zero point which allows for the interpretation of ratio comparisons. Time is an example of a ratio measurement scale. Not only can we say that difference between three hours and five hours is the same as the difference between eight hours and ten hours (equal intervals), but we can also say that ten hours is twice as long as five hours (a ratio comparison)

II. Measurement Scales and Statistical Tests

One of the primary purposes of classifying variables according to their level or scale of measurement is to facilitate the choice of a statistical test used to analyze the data. There are certain statistical analyses which are only meaningful for data which are measured at certain measurement scales. For example, it is generally inappropriate to compute the mean for Nominal variables. Suppose you had 20 subjects, 12 of which were male, and 8 of which were female. If you assigned males a value of ‘1’ and females a value of ‘2’, could you compute the mean sex of subjects in your sample? It is possible to compute a mean value, but how meaningful would that be? How would you interpret a mean sex of 1.4? When you are examining a Nominal variable such as sex, it is more appropriate to compute a statistic such as a percentage (60% of the sample was male).

When a research wishes to examine the relationship or association between two variables, there are also guidelines concerning which statistical tests are appropriate. For example, let’s say a University administrator was interested in the relationship between student gender (a Nominal variable) and major field of study (another Nominal variable). In this case, the most appropriate measure of association between gender and major would be a Chi-Square test. Let’s say our University administrator was interested in the relationship between undergraduate major and starting salary of students’ first job after graduation. In this case, salary is not a Nominal variable; it is a ratio level variable. The appropriate test of association between undergraduate major and salary would be a one-way Analysis of Variance(ANOVA), to see if the mean starting salary is related to undergraduate major.

Finally, suppose we were interested in the relationship between undergraduate grade point average and starting salary. In this case, both grade point average and starting salary are ratio level variables. Now, neither Chi-square nor ANOVA would be appropriate; instead, we would look at the relationship between these two variables using the Pearson correlation coefficient.

Google Maps 6.0 goes indoors

30 Nov

http://download.cnet.com/8301-2007_4-57333103-12/google-maps-6.0-goes-indoors/

Should you buy the $99 Kindle?

8 Aug

 

As CNET’s David Carnoy reported last week, Amazon just dropped the price of the refurbished Wi-Fi Kindle to $99.99. And if you want the 3G model, you can snatch it up for just $129.99. These are the non-ad-supported versions, FYI.

Pretty nice deal, right? They may be refurbs, but they carry the same one-year warranty as new Kindles.

But speaking of “new Kindles,” it’s a poorly kept secret that Amazon will soon unveil one–maybe two. And that begs the question: even at $99.99, is the current Kindle a good deal?

A year ago, I’d have said absolutely. Now, I’m not so sure. The non-touchscreen Kindle is kind of a pain to use, and it’s a pretty limited device–especially compared with something like the Nook Color, which is great not just for books, but also for magazines, apps, and even tablet duty.

(Update: The $179 refurbished Nook Color I wrote about last week? Mine arrived from 1 Sale A Day in about three days. And it looks and operates like new. I’m loving it.)

I know there are die-hard Kindle fans out there, and I’m not here to dis the device. Indeed, I’ve been a rabid e-book fan since PalmPilot days. But I think the days of the monochrome, non-touchscreen e-reader have passed, and I can’t see spending money on one.

What are your thoughts? Are you jumping at the chance to score a Kindle for under $100? Or do you agree that this is, in a way, throwing good money after bad? Let’s hear from you in the comments!

Bonus deal #1: Sony is once again offering the refurbished BDP-S270 Blu-ray player for $59.99 shipped. Among other things, the player can stream Netflix, Pandora, and the like. I bought one a few months back, and it’s been great.

 

 

Bonus deal #2: You know I love me a good Wi-Fi laser printer. OfficeMax has the Samsung ML-1865W Wi-Fi laser printer for $49.99, plus sales tax in most states. Add a box of paper clips or something to pad your cart to over $50, in which case shipping is free.

Read more: http://news.cnet.com/8301-13845_3-20089414-58/should-you-buy-the-$99-kindle/#ixzz1URvQOq5N

Leadership Training Program for Rajarata University MBA students, at JTRT , Kaluthara.

24 Jan

Leadership Training Program for Rajarata University MBA students.

at JTRT , Kaluthara.

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The World’s Fastest Supercomputer Now Belongs to China

2 Dec

Unveiled today at the Annual Meeting of National High Performance Computing (HPC China 2010) in Beijing, Tianhe-1A is the world’s fastest supercomputer with a performance record of 2.507 petaflops, as measured by the LINPACK benchmark.

Tianhe-1A was designed by the National University of Defense Technology (NUDT) in China, and it is already fully operational. To achieve the new performance record, Tianhe-1A uses 7,168 Nvidia Tesla M2050 GPUs and 14,336 Intel Xeon CPUs. It cost $88 million; its 103 cabinets weigh 155 tons, and the entire system consumes 4.04 megawatts of electricity.

Tianhe-1A ousted the previous record holder, Cray XT5 Jaguar, which is used by the U.S. National Center for Computational Sciences at Oak Ridge National Laboratories. It is powered by 224,162 Opteron CPUs and achieves a performance record of 1.75 petaflops.

According to Nvidia, Tianhe-1A will be operated as an open access system to use for large scale scientific computations.

Let Google Drive Your Car

17 Oct

 

 

(October 13, Washington, Sri Lanka Guardian) The Google revolutionized the way we search online. Now the internet giant is looking to revolutionize the way we drive.

Google hooked up models of the Toyota Prius with artificial intelligence software that lets them drive themselves. “The seemingly all-conquering Google has announced that it’s road-testing cars that steer, stop and start without a driver,” Time Magazine reports. “The cars use video cameras on the roof, along with radar sensors and a laser range finder to ‘see’ other traffic.”

Driverless cars seem odd, but Google thinks they’ll be a huge benefit. “Our goal is to help prevent traffic accidents, free up people’s time and reduce carbon emissions by fundamentally changing car use,” Sebastian Thrun, a Google software engineer, states in the company blog.

So far, the new software has been successful. “With someone behind the wheel to take control if something goes awry and a technician in the passenger seat to monitor the navigation system, seven test cars have driven 1,000 miles without human intervention and more than 140,000 miles with only occasional human control,” says the New York Times. “The only accident, engineers said, was when one Google car was rear-ended while stopped at a traffic light.”

Although these cars appear safer, Jalopnik questions their legality — though admits there’s no law barring their existence. “According to California officials, there are no laws that would bar Google from testing such models, as long as there’s a human behind the wheel who would be responsible should something go wrong,” writes Jalopnik.

Californian drivers may spot a Google Prius on the road, but if you live elsewhere, it’ll be a long time before you see a motorist reading behind the wheel while their car drives.

Before you shop, make sure you check out the U.S. News rankings of this year’s best cars. Also, be sure to check us out on Twitter to stay on top of the latest car discounts and incentives.

Here is another article by Sebastian Thrun, Distinguished Software Engineer on the Google Car


What we’re driving at

Larry and Sergey founded Google because they wanted to help solve really big problems using technology. And one of the big problems we’re working on today is car safety and efficiency. Our goal is to help prevent traffic accidents, free up people’s time and reduce carbon emissions by fundamentally changing car use.

So we have developed technology for cars that can drive themselves. Our automated cars, manned by trained operators, just drove from our Mountain View campus to our Santa Monica office and on to Hollywood Boulevard. They’ve driven down Lombard Street, crossed the Golden Gate bridge, navigated the Pacific Coast Highway, and even made it all the way around Lake Tahoe. All in all, our self-driving cars have logged over 140,000 miles. We think this is a first in robotics research.

Our automated cars use video cameras, radar sensors and a laser range finder to “see” other traffic, as well as detailed maps (which we collect using manually driven vehicles) to navigate the road ahead. This is all made possible by Google’s data centers, which can process the enormous amounts of information gathered by our cars when mapping their terrain.

To develop this technology, we gathered some of the very best engineers from the DARPA Challenges, a series of autonomous vehicle races organized by the U.S. Government. Chris Urmson was the technical team leader of the CMU team that won the 2007 Urban Challenge. Mike Montemerlo was the software lead for the Stanford team that won the 2005 Grand Challenge. Also on the team is Anthony Levandowski, who built the world’s first autonomous motorcycle that participated in a DARPA Grand Challenge, and who also built a modified Prius that delivered pizza without a person inside. The work of these and other engineers on the team is on display in the National Museum of American History.

Safety has been our first priority in this project. Our cars are never unmanned. We always have a trained safety driver behind the wheel who can take over as easily as one disengages cruise control. And we also have a trained software operator in the passenger seat to monitor the software. Any test begins by sending out a driver in a conventionally driven car to map the route and road conditions. By mapping features like lane markers and traffic signs, the software in the car becomes familiar with the environment and its characteristics in advance. And we’ve briefed local police on our work.

According to the World Health Organization, more than 1.2 million lives are lost every year in road traffic accidents. We believe our technology has the potential to cut that number, perhaps by as much as half. We’re also confident that self-driving cars will transform car sharing, significantly reducing car usage, as well as help create the new “highway trains of tomorrow.” These highway trains should cut energy consumption while also increasing the number of people that can be transported on our major roads. In terms of time efficiency, the U.S. Department of Transportation estimates that people spend on average 52 minutes each working day commuting. Imagine being able to spend that time more productively.

We’ve always been optimistic about technology’s ability to advance society, which is why we have pushed so hard to improve the capabilities of self-driving cars beyond where they are today. While this project is very much in the experimental stage, it provides a glimpse of what transportation might look like in the future thanks to advanced computer science. And that future is very exciting.