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Corporations who bear the most responsibility for contributing to climate change emissions - and then profiting from those activities - are campaigning to increase their access to international negotiations like the upcoming COP17 meeting in Durban. At the same time, these carbon-intensive industries are working to defeat progressive legislation on climate change and energy around the world.
Our new report, 'Who's holding us back – How carbon-intensive industry is preventing effective climate legislation,' reveals that our biggest barrier to achieving successful, progressive policies at the international, national, and even sub-national levels is the powerful carbon-intensive industry with its vast army of lobbyists, front groups, and political rolodex.
The behaviour of these powerful corporations flies in the face of common sense – with all of the signs telling us that it would be smarter to bring in strong climate laws, and focus on clean, green energy.
Earlier this month we heard that Solar PV had tripled in China over the past year with cost projections far below what was expected. This news was consistent with other exciting news on plummeting costs and technological breakthroughs in the uptake of clean, safe renewable energy around the world this year. At the same time analysis by the European Commission [i] and several independent research institutes [ii] indicate that it would now be in Europe's economic interests to introduce stronger climate legislation. In fact, numerous examples now exist that prove that fighting climate change creates a stronger economy like the Regional Greenhouse Gas Initiative in the US which is creating jobs, lowering electricity rates and at the same time lowering greenhouse gas emissions.
With the economic crisis swirling around us, we constantly hear the cry for 'jobs, jobs, jobs'. And there are additional fears about energy prices hitting families hard. However, it is a mistake to assume that progressive action on climate change is a hurdle to fixing the economy, it is in fact, the very answer the world needs to help solve this crisis. You don't have to take our word for it. Earlier this month the rather conservative International Energy Agency released a report that included a clarion call for quick action and argued that addressing climate change and moving to a clean energy economy is not only an environmental imperative but also good fiscal policy:
"On planned policies, rising fossil energy use will lead to irreversible and potentially catastrophic climate change."
"… we are on an even more dangerous track to an increase of 6°C [11°F]…. Delaying action is a false economy: for every $1 of investment in cleaner technology that is avoided in the power sector before 2020, an additional $4.30 would need to be spent after 2020 to compensate for the increased emissions." (From the 2011 World Energy Outlook [WEO] release summarized here at Think Progress).
The polls show that the public understands the issue – there is in fact a remarkable alignment around the world in support of swift action to address climate change reduce global warming pollution and shift to renewable energy. [iii]
So what's holding us back?
'Who's holding us back,' shines a light on how these actors have undermined or are attempting to undermine progressive climate policies in Europe (e.g., lobbying against a 30% emissions target for 2020), the US (e.g., successfully killed climate legislation in the Senate, opening up new areas to oil extraction), Canada, and South Africa.
This research shows beyond a doubt that there are a handful of powerful polluting corporations who are exerting undue influence on the political process to protect their vested interests.
In this report, we document the tricks of the trade that polluting corporations use to pull the strings of our politicians and mislead the public. We expose the influence pipelines that see these companies play not only our leaders, but entire countries against each other to hold back action on the climate.
Our governments must work with and learn from the business sector but we will not avoid irreversible climate change impacts unless they listen to and act on the behalf of their citizens. In the run up to COP17 its time for the politicians that hold the fate of our economy and environment in their hands in Durban to listen to the people, instead of polluting corporations like Shell, Eskom and Koch Industries.
Read: Who's holding us back – How carbon-intensive industry is preventing effective climate legislation
Tzeporah Berman is the Co-director Climate and Energy Program at Greenpeace International
[i]: European Union. 2010. MEMO/10/215. Climate change: Questions and answers on the Communication Analysis of options to move beyond 20% greenhouse gas emission reductions and assessing the risk of carbon leakage. 26th May 2010.
[ii]: Carlo C. Jaeger, Leonidas Paroussos, Diana Mangalagiu, Roland Kupers, Antoine Mandel, Joan David Tàbara. 2011. A New Growth Path for Europe. Generating Prosperity and Jobs in the Low-Carbon Economy. Synthesis Report.
[iii] Goldenberg, S. 2009. Global poll finds 73% want higher priority for climate change. The Guardian. 30th July 2009.


text: AboutMenus & SpecialsOn TapHistoryBeer To Go! Hours: Monday – Thursday, 11 a.m. 'til 12 midnight Friday & Saturday, 11 a.m. 'til 1 a.m. Sunday, 12 noon 'til 11 p.m. Upcoming Events see all events Oregon Craft Beer Month - February 1–9 Porters & Stouts Saturday, February 01 St. Patrick's Day Tuesday, March 17 Happy 34th Birthday, Ruby! Saturday, March 21 Happy 25th Birthday, Black Rabbit Red! Thursday, April 02 What a long strange trip it's been The Barley Mill — opened in 1983 as the initial joint venture between brothers Mike and Brian McMenamin — is a Southeast Portland favorite, where it has radiated tie-dyed good vibes at the corner of Hawthorne Boulevard and 17th Avenue for 30 years. The cozy Barley Mill is festooned with colorful artwork and artifacts everywhere you look, including some great Grateful Dead memorabilia. Or test your skill on one of our pinball machines. From friends to families, all are welcome — kids especially like our sunny upstairs loft and our outdoor sidewalk seating. Barley Mill Pub allows pets at our sidewalk seating only. Menus Menu Kids Happy Hour & Specials Beverage Neon's List Dietary Restrictions Happy Hour served daily 3-6 p.m. & 10 p.m.-close Drink specials 3-6 p.m. & 10 p.m.-12 midnight No substitutions, additions, or to-go orders Minimum drink purchase of $2 We offer Northwest-style pub fare that incorporates the freshest seasonal ingredients from local and regional growers and producers (including Tillamook Cheese and Ice Cream, Pleasant Valley Pickles, Sheridan Fruit Company, Country Natural Beef and many others). We make everything onsite, including pizza, soups and more. Weekly specials round out our menus – ask your server for details. Gluten-free buns available! These light, crisp, locally made products are available for an additional charge with hamburgers and sandwiches – just ask your server. Please note: Although the bun is gluten-free, our kitchens are not. All Ales Wine Cider Guest Taps Blackberry Cider Blueberry Peach Cider Florence NightingALE Hammerhead Hard Apple Cider Hop On Your Kviek, NorthWest IPA Jam Session IPA McMenamins Irish Stout Nebraska Bitter Puffer Pale Ale Purple Haze Ruby Terminator Stout White Rabbit Coors Light Pabst Blue Ribbon Yum Yum Light The Barley Mill is a touchstone for McMenamins because of its "first" status among our current pubs. However, in history's eye, it's one in a succession of notable watering holes to roost at the corner of Southeast Hawthorne Boulevard and 17th Avenue. The place is flush with great characters and stories dating back to 1934, when Billy Hahn opened the original beer parlor on this spot. It was a year after Prohibition ended and Portlanders were demonstrating a definite thirst for frosty mugs of brew. Hahn called his joint The Scuttlebutt, an old seafaring term for a drinking fountain. "Red" Dorrigan, a Scuttlebutt bartender in the 1950s, epitomized the Scuttlebutt's atmosphere in those early years. A big, tough Irishman, "Red" had auburn hair and a scarlet face, which, one patron remarked, "he didn't get from falling into a strawberry patch." By the middle 1970s, the 'Butt was ending its 30-year run with the time-honored practice of exotic dancers: They danced... and stripped right on the bar. In 1977, new owners reinvented the place with a little more attitude, more surliness, and a new name — Fat Little Rooster. Fifty-five cent Rainiers and pool were definite attractions for the place, but the Rooster's bread and butter was music — loud music! While much of the rest of city's clubs and taverns were featuring glitter and synthesizers, the Rooster strutted gut-bucket blues and other earthy, roots-based music. Some of the Northwest's best bands took their turn tearing the roof off the place, including the Robert Cray Band, Paul deLay's Brown Sugar, Steve Bradley's Sleazy Pieces, the Holy Modal Rounders, and the fabulous Clamtones. After six years of crowing, the Fat Little Rooster flew the coop and area residents took a simultaneous sigh of relief. In 1983, Mike and Brian McMenamin came and scoured the place inside and out and gave the 50-year-old haunt new life as the decidedly more family-oriented Barley Mill Pub. The place was decked out with exuberant neon lights and imaginative murals painted by Norm Forsberg and Joe Cotter. Also, the pub's namesake, a very heavy barley mill (a kitty litter grinder originally) was set up with great effort in the front bay. It's a prized relic rescued from Oregon's first microbrewery, the Cartwright Brewery (1979-1982). Word of mouth brought more people and fun to the place, making the Barley Mill, with its laid-back feel, a comfortable gathering spot for folks of all walks and ages. In particular, the pub's rousing summer anniversary party, with its performers, contests, and specially crafted anniversary ale, continues to be a day of family fun and revelry. Read more Barley Mill History. $8 Growler Mondays Head to any of our pubs every Monday and bring your 2-quart growler to have it filled with a McMenamins craft beer for $8 ($10 for high gravity beers). Regular price $14 - $17. Note: This special does not apply to cider or brewmasters reserves. Check out what's ON TAP on the free McMenamins app. On Tap To Go (not including growler or jar) Hard Cider To Go (not including growler or jar) 1 quart $7.00-$8.50 1 quart $8.80 2 quarts $14.00-$17.00 2 quarts $17.60 Half-Gallon Growlers We're offering these special 64-ounce growlers (think a glass moonshine jug) that are now engraved with many different company seasonal logos and other assorted graphics! Use it time and time again, or keep it as a memento from your visit. Available at all locations Cans To Go Our 16-ounce cans of our signature Ruby and Hammerhead are available to-go at all locations. Crack 'em open and enjoy on the beach, in a backyard....bottle openers are no longer required. Crowlers Offered at our 23rd Avenue Bottle Shop Prices vary We're now offering Crowlers - a mashup of can and growler - in a 32-ounce vessel that we fill fresh from a tap of your choice and seal up to-go. Choose from our 20 rotating taps of McMenamins' and guest breweries handcrafted offerings, including cider and high-gravity selections. Kegs/Cornelius Cans $120/15.5 gallon plus refundable keg and equipment deposits At select breweries we can also fill Cornelius canisters. Call the brewery to make arrangements. You will need to bring your clean canister and leave it with the brewer for filling. You cannot just drop in and get a canister filled unannounced. Current pricing on Cornelius canisters is $50 for 5 gallons, $30 for 3 gallons. If you have a different size Cornelius canister figure on $10 per gallon. Order a keg of McMenamins brew for your next party from one of our Oregon or Washington breweries! Different varieties are available at each location, so call ahead to see what beers are on hand. All sales are final. We do not offer refunds. Shipping Beer Now shipping to anywhere in Oregon Call the Edgefield Gift Shop to order: 503-492-5470 7 a.m.–7 p.m. Pacific Time Bottles: $5.60 per bottle Cans: $2.75 per can Plus shipping and handling Max 24 bottles/cans per order per state law Why can't you ship elsewhere? While Oregon law allows us to ship outside of the state, individual state laws prohibit us to ship beer directly to consumers.

subset: commoncrawl


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Brewers prepare to roll out new entertainment during games
The Brewers' home opener is next Monday afternoon against the Colorado Rockies, and it's not just baseball taking center stage.
These things we've been lugging around all winter, the gloves, the hat, the jacket, no need for them inside Miller Park. While it's all about the cold out there, it feels like spring in here. You can even smell the freshly cut grass.
A week out, there's work happening in every corner, of the ballpark: on the field, in the stands and behind the glass in the control room.
The production crew is getting ready for Opening Day, too.
The Brewers' vice president of entertainment and broadcasting said they started planning for this season in the middle of last season, talking with fans and players, as well as seeing what works in other ballparks.
"And we've taken the off-season to kind of go through and try to make sure that we're doing new things in the ballpark as well as some of the old things that clearly work for us," Aleta Mercer said.
Among the additions this season, WISN 12 News was told are more sing-a-longs and an interactive game similar to Wheel of Fortune.
"What we'd like to do is keep it fresh. We can't sit back on our laurels and say, 'Yup, we've got a really, really great show.' Instead, what we'd like to do is say, 'Yeah, we have a really good show and we think we can do even better,'" Mercer said.
Fans will get a taste of what's to come when they test out some of the new entertainment options Friday and Saturday for the Brewers exhibition games here at Miller Park. Otherwise, the big reveal comes next Monday on Opening Day.
Tickets for the weekend exhibition games on Friday and Saturday are still available.


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- Take small steps back to speaking: I recommend four stepping-stone speaker experiences to newbies, and they're a great way for the experienced, but out-of-practice, speaker to get her toe back in the water. Ask a question at a talk, chair a session, moderate a panel or be a panelist--these steps all stop short of shouldering all the responsibility for a full-length speech, but give you easy and visible ways to practice (and to show your willingness to speak.)
- Take the time to consider what you want to change: You may hesitate if you lack certain skills or feel you want to step up your game. Think about and write down what you want to change in your approach to speaking, then seek out group training or individual coaching focused on just those aspects of your talks.
- Watch or listen to some expert speakers online to get new ideas: Why not try something new? I recommend three websites where you can watch or listen to some of the world's best and most creative speakers and storytellers for inspiration and ideas.
- Be brief. Leave the audience wanting more and choose to re-start your speaking with a briefer-than-usual talk. Then take the time to analyze how you did and where expansion of your points makes sense.
- Get prepared. Work with a coach or speechwriter to develop a short, three-point message or a formal, prepared speech. Working from a text may help you feel more confident when you re-enter the speaking sphere.
Related posts: 4 stepping stones to get speaking practice
Learn storytelling online, 3 ways


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Mitt Romney could face new questions about his overseas investments after a campaign official acknowledged to NBC News that his campaign is revising his federal ethics forms to report more than a half dozen offshore holdings, including income from a multi-million dollar Swiss bank account that was not disclosed last year.
The tax returns released by the Romney campaign this week showed that the Ann Romney Blind Trust had reported $1,783 in interest income from a bank account held at UBS in Switzerland in 2010. But interest from the Swiss bank account -- as well as holdings in other offshore investments in the Cayman Islands, Bermudas and Ireland that appear in the trust fund's tax returns -- were not disclosed in Romney's financial disclosure form filed with the Office of Government Ethics last August.
A Romney campaign official emailed Thursday afternoon that Romney's financial disclosure form is now being amended with the government ethics office "to address this minor discrepancy" and "to deal with some other minor issues." The Romney campaign's decision to amend the forms, and additional details about the failure to report the overseas holdings, was also reported Thursday by Los Angeles Times.
The number and size of Romneys' offshore investments have lately emerged as a major campaign issue, putting the former Massachusetts governor on the defensive over his wealth (estimated at up to $250 million) and forcing his campaign to release his 2010 tax returns this week. When the campaign released the returns on Monday, it arranged for R. Bradford Malt, a longtime lawyer for the Romneys, to brief reporters in a conference call about them.
But while the returns have produced no revelations about any improper dealings on Romneys' part, they have continued to raise questions about how and why some of his multiple overseas investments were made in the first place, and why there were not more fully reported on Romney's financial disclosure forms.
Malt said he opened up the Swiss bank account, holdings in which are valued at between $1 million and $5 million, in 2003 on behalf the Ann Romney trust in order to provide "international currency diversification" for the trust's holdings. He then shut it down in early 2010, he said, and transferred the assets to the United States, noting that Romney was preparing to run for president at the time and he did not want to have it become an issue in the presidential campaign.
"I was worried that people would write stories not understanding this," said Malt, who administers the blind trust on behalf of the Romneys.
But Malt apparently did not act quickly enough, given that the interest income had to be reported on the trust's tax returns for 2010. (He would not say when in 2010 the account was closed.)
The account was first disclosed as one of Romney's holdings when he filed a financial disclosure form in 2007 after he launched his first campaign for president. Malt acknowledged today that it should have actually been reported as a holding of the Ann Romney blind trust and that Romney's 2007 financial disclosure form is also being amended to correct what he described as a "clerical error."
The campaign had no immediate explanation for why the $1,783 in interest income from the UBS account had not been included in the 2011 financial disclosure, an omission noted by political rivals. The Democratic National Committee hastily organized a conference call Thursday afternoon to seize on the revelation.
Malt initially insisted in an interview that the amount was below the threshold that needed to be reported. But one category on the standard government financial disclosure form specifically asks candidates to report assets valued at between $1,000 and $5,000 as well as income from any holdings of between "$1,001 and $2,500." A campaign official later said Malt had been misunderstood, acknowledging that both the 2007 and the 2011 financial disclosures the campaign filed are being revised.
But the inclusion of the Swiss bank account is apparently not the only revision. The Ann Romney blind trust also reported income from shares in offshore companies such as Barricuda Investments Ltd. in Ireland, Castle Garden Funding in the Cayman Islands, and Sankaty High Yield Assets Investors Ltd. in Bermuda. Those holdings also were not included in the financial disclosure form that Romney filed last August. The form requires presidential candidates to report all assets and income for them and their spouses for the full calendar prior to the year of the filing.
But Malt said there had been no attempt by the Romneys to conceal these investments, and all taxes on their earnings were fully paid. Malt stressed that the Ann Romney trust fund was simply a passive shareholder in the investments funds, which in turn owned shares in the offshore companies. He said the investing entities were like "mutual funds."


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Orlando, FL -- (SBWIRE) -- 05/09/2016 -- Concrete is one of the most unique products in the world of construction because it mixes several basic materials and has specific features. Floors in garages, decks, warehouses, basements, manufacturing plants, storage rooms and other similar areas are usually covered with concrete floor coating. Concrete is a type of material that can be used in any area regardless of the weather. Of course, in order to enjoy all the benefits that concrete coatings bring, you will have to find a reliable concrete coating company.
Businesses and individuals in Florida can always count on Florida Epoxy as one of the leading companies in the field of concrete coating installation, concrete coating removal, concrete polishing and concrete staining. Florida Epoxy has a very experienced and highly-qualified team of specialists who have already provided quality concrete coatings to many different facilities.
There are many business owners who hesitate to change their existing damaged coating or invest tin new coating because they are not sure how costly this change is. These business owners will be glad to hear that Florida Epoxy now provides free consultation and quote. According to the management of this company, they know how important time and money are and this is the reason why they allow their potential clients to use their online form for getting free quote and consultation. The users only have to choose the type of project, the timeframe and their status in order to get a free quote. Once this information is submitted, it won't take much time before the representatives of Florida Epoxy will contact the sender and deliver an offer or explanation for anything they want to know. The best part is that the submitter doesn't have any obligation.
About Florida Epoxy
Florida Epoxy provides such services in Miami-Dade County, Palm Beach County and Broward County. They have already worked with many clients who had businesses in different industries. Some of the industries where they have experience include: storage facilities, auto dealerships, airport hangars, parking garages, sports arenas, warehouse floors, industrial floors, hospitals, stadiums and any place where concrete coating can be applied. Obviously, their services are available to homeowners too.
If you want to find out more about the advantages of concrete coating or you want to learn more about Florida Epoxy's offer, you can always use their official website http://www.floridaepoxy.com/
Company Name: Florida Epoxy
Phone: (954) 302-2247
Email: email@example.com
URL: http://www.floridaepoxy.com/


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Google is aiming to supe up YouTube by offering streaming TV, for a fee.
While the people at Google and YouTube have kept silent about the streaming TV idea, Peter Kaftka of MediaMemo claims that sources close to him have revealed that negotiations are taking place between the Web giants and the major TV networks.
If true, this is the latest attempt to compete with Apple's iTunes, Amazon, the PlayStation store, and a growing number of other on-line TV deals. It is also one more card in the deck falling from free to fee when it comes to Web content.
Google's official statement is that they do not comment on rumors but say they hope to expand their relationship with movie studios and television networks when it comes to the selection and type of content they offer to their community.
YouTube already streams some mainstream media TV shows for free, but falls far behind in comparison to their would-be competitors.
U.S. & World
Stories that affect your life across the U.S. and around the world.
The immediate questions that come to mind are what prices are Google considering and whether or not they will be able to undercut other on-line offers.
And if Google can somehow offer cheaper prices than their competitors for the same content, the major networks may be forced to rework their contracts with the likes of iTunes and Amazon, the current leaders in the paid on-line TV show business.
The future of on-line media consumption is a given in today's tech driven society and if Google wants in on the action they might have to consider what Hulu, the leader in free on-line TV, and iTunes are doing to stay competitive. Hulu is jumping on the Netflix bandwagon and offering a subscription based services with a flat (or fat) monthly fee.
At this point, any new revenue stream for YouTube other than ad dollars would be pure profit for Google, who acquired the company in November 2006. And since Google has failed thus far in slowing the appeal of the iPhone with its new Google phone, iTunes may look like an easier target.


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It lasted less than three weeks, from Oct. 23 until Nov. 10, but the Hungarian Revolution that convulsed Budapest and the rest of Hungary in late 1956 sent shock waves through eastern and central Europe that reverberated for decades. More than a few historians, in fact, cite the popular revolt as the first rip in the Cold War's Iron Curtain.
The general lineaments of the 1956 conflict are well-known: In the autumn of that year, hundreds of thousands of Hungarians, in cities and the countryside, rose up against occupying Soviet forces and, critically, against the country's brutal, homegrown secret police, the State Protection Authority. For a few heady weeks, it seemed like the insurgents might actually push the Russians out altogether. By mid-November, though, the Soviet army had regrouped and launched an all-out assault on a nation that was, nominally, both an ally and a protectorate.
Roughly 3,000 Hungarian civilians—men, women, children—were killed during those three weeks. The uprising was crushed. But the ripples of the revolt—the Prague Spring in '68, Poland's Solidarity trade union movement in the 1980s and other rebellions, large and small—were wide-ranging, and long-lasting.
Here, LIFE.com recalls the events through photos made by the great Michael Rougier, a photojournalist whose pictures routinely conveyed both an unmistakable authority and a seductive intimacy—often in the very same frame.
In its Nov. 12, 1956, issue, meanwhile, in an article titled, "Patriots Strike Ferocious Blows at a Tyranny," LIFE magazine commemorated the short-lived uprising like this:
For three incredible days in Hungary last week the flames of liberty and revenge against tyranny rose high. It almost seemed as if they could go on burning. The pictures on this and the following pages document this terrifying and exhilarating event.
Rebel patriots stormed recklessly toward freedom, Communist henchmen reaped the frightful wrath they had sowed. The most hotly hated of the rebels' targets were the Soviet-controlled Hungarian secret police. These were cut down as ruthlessly as they themselves had murdered countless anti-communists. Soviet occupation troops felt the national fury. Daredevil teenagers burned up their tanks with "Molotov cocktails" until Soviet columns evacuated Budapest, leaving their dead behind them. Most of the Hungarian army, siding with the rebels, stood off Soviet troops throughout the country. Workers not engaged in the fighting went out on a general strike against Communism.
The Soviets struck back with old-time Stalinist savagery. They poured reinforcements in, ringing Budapest. They encircled the Hungarian army in the provinces. At the end of the week, the Budapest radio burst out brokenly: "Russian MiG fighters are over Budapest. . . . The Russian infantry division is going toward the parliament. . . . Gyor is completely surrounded. . . . Pecs was attacked. . . . The Russians are using phosphorous bullets. . . . We shall die for Hungary and Europe. . . . Any news about help? Quickly, quickly, quickly . . ." Then Budapest fell.
A month after Soviet troops had retaken the capital and the rest of the country, LIFE published another major article on the insurrection—this time singling out the fearlessness of Hungary's women for praise:
In Budapest last week . . . the Russian masters and their desperate Hungarian puppets faced a new and formidable foe. The city's women, some of whom had fought earlier at the side of their men and then had bitterly buried the men who had fallen, suddenly banded together in a series of fresh demonstrations of defiance.
"Only women are wanted this time," they shouted as they joined up in the streets. Then, ignoring the ominous presence of security police and Russian tanks, they marched with flowers and flags to a service commemorating their dead. The men doffed their hats in tribute as the women paraded past and joined with them in the stirring words of a forbidden song—"We shall never be slaves."
Thirty-three years to the day after the start of the uprising, on Oct. 23, 1989, president Mátyás Szűrös officially declared the establishment of the Hungarian Republic, replacing in an instant the Hungarian People's Republic. A few weeks later, on Nov. 9, 1989, thousands of people attacked a brute, concrete symbol of the Cold War—and of the Warsaw Pact's illegitimacy—not with rifles and Molotov cocktails, but with sledgehammers and pickaxes. Four decades after the Hungarian Revolution had ended in defeat, the Berlin Wall—which had not yet been built when Budapest first rose up against the USSR—peacefully fell.
The Cold War was over.
Ben Cosgrove is the Editor of LIFE


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Hi all
I signed up purely for somewhere to discuss a reasonable subject with some hopefully reasonable people.
I live life by the simple classic philosophy of "live and let live". Whatever you believe, fine - get on with it, don't push it on me, don't push it on anyone else, and don't let it harm anyone. If everyone could stick to that, we'd have no problems.
The straw that broke the camel's back for me tonight was yet another great TV program turning to god. It happened with Sons of Anarchy (episode after episode about finding god and jesus etc) which was infuriating. I don't mind if it's just a part of the plot - part of people's lives etc, that's normal. But when it starts to spread over several episodes, with the main character having some sort of patronising revelation, it becomes condescending.
It happened with Lost too - the final series being the worst thing to ever happen to an awesome TV show ever - and it completely ruined what had been an amazing show for me.
Then tonight I'm watching Dexter (I'm on season 6 now) and he's suddenly transforming from a science-based hardcore atheist, into a believer over night because of some story involving his kid getting ill (I won't ruin it in case you've not seen it). So now they're shoe-horning religion in, using a sick child as leverage, which is emotional blackmail at its finest. It just cheapens the entire show... it's about a scientist who looks at life using only logic - black and white facts. Then in the course of about 3 episodes, he's starting to "see the light" and believe? Absolutely infuriating.
I shouldn't get so wound up about it - it's just a TV show - but do you ever feel like the writers/producer are piggybacking their belief system onto the show? It really gets on my nerves. Whether or not you believe in god, must it be rammed down our necks during EVERY decent TV show?
On a side note, I noticed that when signing up here there's a warning to say that your name will show up in google results, so you can use a pseudonym. Do we really live in a society where people have to hide their status as an atheist? It's just utterly bizarre, we're meant to be an educated advanced civilisation!
Argh, rant over - apologies, I know it's all pointless, I'm just a bit annoyed at another show being spoiled.
Views: 402
A pregnant pause?
(I can't really see you using that as a nom de plume, however)
It would be funny to post as Pregnant Pause though. I keep chuckling about how that would look, and what would be posted.
I tried to have a pregnant pause, but then my water broke.
It's a way for some producers to sneak in their own proclivities. Ever notice how no one smokes on TV anymore? It's not forbidden, but it's not encouraged, and not written into scripts.
Once the screenwriter is finished, the director goes to work with his red pencil - if you look at the rolling credits, you may see several names following the words, "Screenplay by," this is because once modifications to the script has been made by the director, the script may be handed off to another writer to rewrite, and thereby earning credit (though not top billing). Then again, the producer has veto power, and above that, network executives can decide to order a rewrite to include their point of view, or threaten to pull the plug on the piece.
Hi, Fadi - are you new here, or am I just seeing you for the first time? If you're new, you may not know that we have a special forum where members from Egypt, as well as other Islamic countries share their experiences, in case you'd care to join us: http://www.thinkatheist.com/group/egyptianatheists?xg_source=msg_we...
Please, please, please be safe. Watch what you post under your real name.
What I want to know is: are there any pop culture stories in which a main character comes around to atheism? If so, are there any stories in which this is not treated as a tragedy and/or the character is not the antagonist?
Good point. Perhaps we should make one!


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Officially announced at E3 2008, the game was originally slated for a fall 2009 release. Not wanting to be squashed beneath the weight of another FPS you may have heard of, Activision bumped the game to early 2010. But when the company's financial outlook emerged in February, June became the new tentative release date.
Today, with the release of the Last Resort trailer, Activision specified the game will see the light on June 29.
The Singularity synopsis from the developers:
In Singularity, you will fight your way through an ever-shifting environment haunted with time ravaged creatures, while sudden time waves hurl you back and forth between 1950 and present day. Use your wits and the perfect weapon-the Time Manipulation Device - to unravel the conspiracy on the remote island of Katorga-12.
Sharkey says: Based on the trailer, it looks like Raven Software made the best of the delays -- Singularity looks great. I'm not a huge fan of time travel mechanics in games, but it looks like this one has been polished until it shines.


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SCC's Women's Golf team has been selected as a California Community College Athletic Association Scholar Team recipient. The award emphasizes academic achievement of all members of a specific team. Additionally, SCC athlete Daniel Sayles has been selected as the 2013 CCCAA Male Scholar Athlete. It is the highest honor that the CCCAA awards to student athletes among the 27,000 annual participants.
SCC's student athletes' accomplishments will be acknowledged at the Celebration of Student Athletes Awards Luncheon at the CCCAA Convention on Wednesday, April 2.
To learn more about Panther Athletics, visit the official website!


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For several years now, I've been reading and talking with people about the advantages of becoming a locavore*, a person who eats only food grown, farmed, or produced locally.
I've learned a lot. I could go on at length about why eating locally is more sustainable, is often healthier than typical grocery store food that's been transported an average distance of 1500 miles, and why it's good for the earth and all living things in many other ways, but instead I'll refer you to three books that got me interested in the first place. Continue Reading


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BTE TRYOUT THREAD #6You awaken feeling strangled, coughing and choking as you fall to your hands and knees. Your eyes open wearily - and you see nothing. Gasping for air, you glance about wildly, disoriented, and realize to your horror that you're in the middle of a vast, black emptiness. Terrified, you instinctively grasp at the void about you for something - anything - solid. You realize that despite the enveloping darkness, your arms glow with a strange light - suddenly you pause and rack your brain for any hint as to what's going on. The last you remember clearly is being sold to Tartarus Inc. You remember them strip-searching you, your last possessions taken away before the coarse-mannered guards threw you into an empty cell. Everything about it seems a struggle to recall - clouded and fogged as though a distant memory.
Brom and Trontos (Hema and BFett)
Brom and Trontos (Hema and BFett)
Welcome, a cold, hard voice speaks. It's not the infamous SCAMPS of Tartarus - it's a woman's voice: cruel, hard, and utterly devoid of sympathy. You're wondering what you're doing here. Try not to trouble yourself about it.
A glow brightens to your right - in surprise and slight fear, you scoot back across the floor as you try to assess the new threat. A human form, dressed in a Tartarus jumpsuit, appears and crumples to the floor, choking and gasping for breath.
I'm so sorry, the voice says with an air that makes it clear her words are merely a formality. I understand that this experience is mentally agonizing. We're far from perfecting cryotech, you see, but you are here to help us correct that. You are our test subjects. If you ever want to awaken, both of you will cooperate. You've always cooperated with us before, so I have no doubt you'll do so now.
Your mind whirls. You vaguely remember being pulled from your isolation cell - you remember being marched down a hallway, blindfolded, cuffed and gagged - something injected into your arm. That's all you remember. Cryotech? That doesn't even exist...
Then it sinks in: You've always cooperated with us before. "Before???"
It's "brain maintenance," the voice explains in a resigned, curt tone, as though she's said it a hundred times. Cryotech has a tendency to leave one vegetative. Now, the both of you... make your decision, and we will begin.
The void hums, and shivers to life - six pale, golden hexagons of light appear pm the floor around you, spaced well apart. On each: a short paragraph of text.
You feel like you've done this before.
But which to stand upon? And who is this person next to you?
Alien sporeship
Your small salvage team recently chanced upon a migrating Esti'la sporeship. Attacking them is risky (and illegal), but they sometimes feed on human colonies anyway, and your crew is of the opinion that they deserve it. More than that, just a few kilos of juices from their larval pods can sell for tens of thousands.
This tile has the silhouette of a spaceship underneath the text.
Monster hunter
A large alien beast, an enchridnid, has been sighted rampaging near your colony on the outworld planet Okkrla. Hunt the thing back to its lair, and put an end to it so that it won't be able to cause any harm in the future.
This tile has the silhouette of a spaceship underneath the text.
Black market
Deep inside an asteroid-based space station, a black market trading deal goes terribly wrong. If you intend to survive the ensuing shootout, you'll need your wits about you - and your guns.
This tile has the silhouette of a spaceship underneath the text.
Colony eradication
Wipe out the Mataduktun colony on a nearby moon. Nobody likes a Mataduktun, and it's really lowering the property values. Worse, it's likely to attack attention from the coreworlds, and that's the last thing you want. It would put an end to your pirate operation.
This tile has the silhouette of a spaceship underneath the text.
Fleeing suspect
A major suspect in one of your recent investigations has figured out you were tailing him and fled into "the bad side of town" on Orthus Etienne V - specifically, a hideout for a major local gang, the Black Serpents. You're left with no choice but to follow him through and hope you emerge unscathed.
Something different
You are thrown into a random mission without any idea of what you're up against. It may or may not suit your abilities and loadout.
A question mark dominates this particular hexagon.
Spoiler: SHOW
- This is canon, but your character does not necessarily need to remember it. You have the OOC option to have a mindwipe of the experience post-tryout/pre-campaign. Whether your teammate's character remembers the experience is up to them.
- This is VR. You know this, but your character does not necessarily know this. Whether your character can figure it out is up to your discretion.
- A spaceship silhouette means the mission is CASKET-based.
- Your mission selection is quasi-randomly assigned with a weighted algorithm that takes your personalities, stats, skills, and official loudout into account.
- On average, half your tryout points will come from the tryout mission itself. You are being judged from the moment the thread is posted.
- Each team will have their own tryout thread. Do not post in someone else's thread.
- Type in ((out of character parentheses)) in the tryout thread if you feel it necessary. No need to post elsewhere - it's your thread and no one else's, so you needn't feel bad about cluttering it up.
- There are no maps. As this is VR, it uses "fluid space" as REKT VR traditionally has - that is to say, if you imagine something is there, it may well be there (unless I say it isn't). In a forest and need cover? Say there's a tree next to you and duck behind it. This will probably work. Want a bigger weapon? Say you pick up your death ray from behind the counter - but don't be surprised if I say there's nothing there. Use common sense and don't try to cheat the system - and above all, ignore nothing that I say.
- To activate your choice, both of you must stand on your golden hexagon.
- If you die, that's okay. If you die within the first turn or two, that's not okay.
- Don't be surprised if I cut the mission short. I almost certainly will. These are not meant to be full missions - it is merely a sample to judge from.
- Asking for ideas from other people is completely permitted. Offering ideas is completely permitted as well - as long as you don't do it in someone else's thread.
- Poor performance of your teammate does not mean you do any more poorly in the tryout. You are judged separately.
- I will be judging a number of things, but can't give specifics without compromising the judging process.
- Have fun! If it's not fun for you, let me know, and I'll try to switch things around.


text:
I just purchased a 2015 Requirements book. I hate doing so since they're available all over for free, but I really wanted to get a look at the two new badges. I recently assumed the role of Merit Badge Counselor Coordinator for my district, and one of my responsibilities is to recruit MBCs when a new badge is released.
For Animation, I can go to a company that does graphics for websites and other media and recruit professionals who are recognized as having skills and education in the subject matter. Where do I turn for this one? The Scout needs to send or receive messages in Morse code, ASL semaphore and Braille. There are also requirements related to traffic signs, topo map symbols, texting symbols and even aircraft carrier catapult crew signals. Who on earth is qualified to sign off on all these requirements?
Sorry for the rant. I guess I'm just old and out of touch. In my day there were only two signals. The emperor either put his thumb up or down to indicate whether the gladiator should live or die.


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Canonical Correlation. Canonical Correlation is used to investigate the relationship between two sets of variables (it is used as either a hypothesis testing or an exploratory method). For example, an educational researcher may want to compute the (simultaneous) relationship among three measures of scholastic ability with five measures of success in school. A sociologist may want to investigate the relationship between two predictors of social mobility based on interviews, with actual subsequent social mobility as measured by four different indicators. A medical researcher may want to study the relationship of various risk factors to the development of a group of symptoms. In all these cases, the researcher is interested in the relationship between two sets of variables, and Canonical Correlation is an appropriate method of analysis. See the Canonical Correlation overview for more information.
C&RT. C&RT, or Classification and Regression Trees, is a classification tree program developed by Breiman et. al. (1984).
Classification and Regression Trees are analytic procedures for predicting the values of a continuous response variable (e.g., Height) or categorical response variable (e.g., Marital Status: Single, Married, Divorced) from continuous or categorical predictors. When the dependent or response variable of interest is categorical in nature, the technique is referred to as Classification Trees; if the response variable of interest is continuous in nature, the method is referred to as Regression Trees.
The classic computational algorithms for classification and regression trees (C&RT) were popularized by Breiman, Friedman, Olshen, & Stone, 1984 (see also Ripley, 1996; Hastie, Tibshirani, & Friedman, 2001). For classification problems, the goal is generally to find a tree where the terminal tree nodes are relatively "pure, " i.e., contain observations that (almost) all belong to the same category or class; for regression tree problems, node purity is usually defined in terms of the sums-of-squares deviation within each node.
At each step, the program will find a logical split condition to assign observations to the two child nodes; for continuous predictors these logical conditions are usually of the type If x > Value then NodeID=k; for categorical predictors, the logical split conditions are usually of the type If x=Category I then NodeID=k. For more details, see STATISTICA Classification and Regression Trees (C&RT).
Cartesian Coordinates. Cartesian coordinates (x, y, or x, y, z; also known as rectangular coordinates) are directed distances from two (or three) perpendicular axes.
The location of a point in space is established by the corresponding coordinates on the X-and Y-axes (or X-, Y- , and Z-axes). See also, Polar Coordinates.
Casewise Missing Data Deletion. When casewise deletion of missing data is specified, only cases that do not contain any missing data for any of the variables selected for the analysis will be included in the analysis. In the case of correlations, all correlations are calculated by excluding cases that have missing data for any of the selected variables (all correlations are based on the same set of data). See also, Casewise vs. Pairwise Deletion of Missing Data.
Categorical Dependent Variable. A categorical dependent variable is a variable of interest (a researcher wants to predict), measured on a nominal scale, whose values identify class or group membership (e.g., Gender, with classes Male and Female; or Education, with classes No High School Degree, High School Degree, Some College, College Degree, Some Graduate School, Graduate Degree). A researcher may be interested in predicting the group membership of observations based on the values of some independent or predictor variables. For example, Credit Risk, with values Good and Bad, would be a categorical dependent variable that we might want to predict based on measured independent variables that are possibly related to credit risk.
Categorical Predictor Variable. A categorical predictor variable is a variable, measured on a nominal scale, whose categories identify class or group membership, which is used to predict responses on one or more dependent variables. Gender is an example of a categorical predictor variable, with the two classes or groups Male and Female. See also, nominal scale of measurement.
Categorized Graphs (Also, Trellis Graphs). This type of graph is used to categorize 2D, 3D, or nD plots by the specified categories of a selected variable. One component graph is produced for each level of the grouping variable (or user-defined subset of data) and all the component graphs are arranged in one display to allow for comparisons between the subsets of data (categories). For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques; see also Data Mining.
Categorized Plots, 2D - Detrended Probability Plots. This categorized plot is constructed in the same way as the standard normal probability plot for the categorized values, except that before the plot is generated, the linear trend is removed. This often "spreads out" the plot, thereby enabling the user to detect patterns of deviations more easily. For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorized Plots, 2D - Half-Normal Probability Plots. The categorized half-normal probability plot is constructed in the same manner as the standard normal probability plot, except that only the positive half of the normal curve is considered. Consequently, only positive normal values will be plotted on the Y-axis. This plot is often used in plots of residuals (e.g., in multiple regression) when we want to ignore the sign of the residuals, that is, when we are interested in the distribution of absolute residuals regardless of the sign. For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorized Plots, 2D - Normal Probability Plots. This type of probability plot is constructed as follows. First, within each category, the values (observations) are rank ordered. From these ranks, we can compute z values (i.e., standardized values of the normal distribution) based on the assumption that the data come from a normal distribution (see Computation Note). These z values are plotted on the Y-axis in the plot. If the observed values (plotted on the X-axis) are normally distributed, all values should fall onto a straight line. If the values are not normally distributed, they will deviate from the line.
Outliers may also become evident in this plot. If there is a general lack of fit, and the data seem to form a clear pattern (e.g., an S shape) around the line, the variable may have to be transformed in some way (e.g., a log transformation to "pull-in" the tail of the distribution, etc.) before some statistical techniques that are affected by non-normality can be used. For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorized Plots, 3D - Contour Plot. This type of graph projects a three-dimensional surface onto a two-dimensional plane as contour plots for each level of the grouping variable. The plots are arranged in one display to allow for comparisons between the subsets of data (categories).
For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorized Plots, 3D - Deviation Plot. Data points (representing the X, Y, and Z coordinates of each point) in this graph are represented in 3D space as "deviations" from a specified base level of the Z-axis. One component graph is produced for each level of the grouping variable (or user-defined subset of data) and all the component graphs are arranged in one display to allow for comparisons between the subsets of data (categories). For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorized Plots, 3D - Scatterplot. This type of graphs visualizes a relationship between three variables (representing the X, Y, and one or more Z [vertical] coordinates of each point in three-dimensional space) categorized by a grouping variable. One component graph is produced for each level of the grouping variable (or user-defined subset of data), and all the component graphs are arranged in one display to allow for comparisons between the subsets of data (categories) (see graph number 1, below).
For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques. See also, Data Reduction.
Categorized Plots, 3D - Space Plot. This type of graph offers a distinctive means of representing 3D scatterplot data through the use of a separate X-Y plane positioned at a user-selectable level of the vertical Z-axis (which "sticks up" through the middle of the plane). The level of the X-Y plane can be adjusted in order to divide the X-Y-Z- space into meaningful parts (e.g., featuring different patterns of the relation between the three variables) (see graph number 2, above). For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorized Plots, 3D - Spectral Plot. This type of graph produces multiple spectral plots (for subsets of data determined by the selected categorization method) arranged in one display to allow for comparisons between the subsets of data. Values of variables X and Z are interpreted as the X- and Z-axis coordinates of each point, respectively; values of variable Y are clustered into equally spaced values corresponding to the locations of the consecutive spectral planes (see graph number 3, above). For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorized Plots, 3D - Surface Plot. In this type of graph , a surface (defined by a smoothing technique or user-defined mathematical expression) is fitted to the categorized data (variables corresponding to sets of XYZ coordinates for subsets of data determined by the selected categorization method) arranged in one display to allow for comparisons between the subsets of data (categories).
Categorized Contour/Areas (Ternary graph). This 3D categorized plot projects a three-dimensional surface onto a two-dimensional plane as area contour plots for each level of the grouping variable. One component graph is produced for each level of the grouping variable (or user-defined subset of data), and all the component graphs are arranged in one display to allow for comparisons between the subsets of data (categories).
Categorized Contour/Lines (Ternary Graph). This 3D categorized plot projects a three-dimensional surface onto a two-dimensional plane as line contour plots for each level of the grouping variable. One component graph is produced for each level of the grouping variable (or user-defined subset of data), and all the component graphs are arranged in one display to allow for comparisons between the subsets of data (categories). For a detailed discussion of Categorized Graphs, see Categorized Graphs in Selected Topics in Graphical Analytic Techniques.
Categorizing, Grouping, Slicing, Drilling Down. One of the most important, general, and powerful analytic methods involves dividing ("splitting") the data set into categories in order compare the patterns of data between the resulting subsets. This common technique is known under a variety of terms (such as breaking down, grouping, categorizing, splitting, slicing, drilling-down, or conditioning), and it is used both in exploratory data analyses and hypothesis testing. For example: A positive relation between the age and the risk of a heart attack may be different in males and females (it may be stronger in males). A promising relation between taking a drug and a decrease of the cholesterol level may be present only in women with low blood pressure and only in their thirties and forties. The process capability indices or capability histograms can be different for periods of time supervised by different operators. The regression slopes can be different in different experimental groups.
There are many computational techniques that capitalize on grouping and that are designed to quantify the differences that the grouping will reveal (e.g., ANOVA/MANOVA). However, graphical techniques (such as categorized graphs) offer unique advantages that cannot be substituted by any computational method alone: they can reveal patterns that cannot be easily quantified (e.g., complex interactions, exceptions, anomalies) and they provide unique, multidimensional, global analytic perspectives to explore or mine the data.
Cauchy Distribution. The Cauchy distribution (the term first used by Uspensky, 1937) has a density function:
f(x) = 1/(*{1 + [(x-)/]2})
0 <
is the location parameter (median)
is the scale parameter
is the constant Pi (3.14...)
The animation above shows the changing shape of the Cauchy distribution when the location parameter equals 0 and the scale parameter equals 1, 2, 3, and 4.
Cause-and-Effect Diagram. The Cause-and-Effect diagram provides an efficient summary of factors that impact a process, and hence can be used as a map to guide the overall quality improvement efforts. Therefore, it is one of the important tools for the Define phase of Six Sigma quality control efforts. The diagram is also sometimes referred to as a "fishbone chart," because of its appearance, or an Ishikawa chart. The latter name refers to the work of Professor Kaoru Ishikawa of Tokyo University who developed this diagram to depict variables that are present in a process. The general idea of the chart is rather straightforward. Suppose we want to turn on a reading light in your house one evening, and it won't light up. Now consider the various variables or characteristics that make up the process (cause the light to come on), and which should be considered in order to fix this quality problem:
The cause-and-effect diagram shown above (adapted from Rath & Strong's Six Sigma pocket guide, 2000) spells out the various potential causes of the problem encountered. Usually, the chart is constructed by identifying (1) the major categories of causes that affect the process (in this example Power, Bulb, Plug/Cord and Lamp), and (2) the individual factors or causes that can be classified into these major categories (e.g., Power outage, No house current, etc.). We could now use this map as a guide to troubleshooting the problem we encountered turning on our reading light. We can also further "augment" this chart by adding various sub-sub causes, causes that we ruled out, solutions we have tried, etc.
The cause-and-effect diagram plays a central role in Six Sigma quality programs. During the first stage of the Define-Measure-Analyze-Improve-Control (DMAIC) cycle, this diagram can be of great utility in order to identify the areas, departments, processes, and stakeholders that should be involved in the effort. See Harry and Schroeder (2000), Pyzdek (2001), or Rath and Strong (2000) for additional details; see also the Six Sigma topic.
Censoring (Censored Observations). Observations are referred to as censored when the dependent variable of interest represents the time to a terminal event, and the duration of the study is limited in time. Although the concept was developed in the biomedical research, censored observations may occur in a number of different areas of research. For example, in the social sciences we may study the "survival" of marriages, high school drop-out rates (time to drop-out), turnover in organizations, etc. In each case, by the end of the study period, some subjects probably will still be married, will not have dropped out, or will still be working at the same company; thus, those subjects represent censored observations.
In economics we may study the "survival" of new businesses or the "survival" times of products such as automobiles. In quality control research, it is common practice to study the "survival" of parts under stress (failure time analysis).
Data sets with censored observations can be analyzed via Survival Analysis or via Weibull and Reliability/Failure Time Analysis. See also, Type I and II Censoring, Single and Multiple Censoring and Left and Right Censoring.
Central Limit Theorem. Let Σ y denote the sum of a random sample of n measurements from a population having a mean, μ, and finite standdard deviation σ. Let μΣy and σΣy denote the mean and standard deviation of the sampling distribution of Σ y respectively. Based on repeated random samples of size n from the population, we can conclude the following:
CHAID. CHAID is a classification trees program developed by Kass (1980) that performs multi-level splits when computing classification trees. For discussion of the differences of CHAID from other classification tree programs, see A Brief Comparison of Classification Tree Programs.
Characteristic Life. In Weibull and Reliability/Failure Time Analysis the characteristic life is defined as the point in time where 63.2 percent of the population will have failed; this point is also equal to the respective scale parameter b of the two-parameter Weibull distribution (with = 0; otherwise it is equal to b+).
Chi-Square Distribution. The Chi-square distribution is defined by:
f(x) = {1/[2/2 * (/2)]} * [x(/2)-1 * e-x/2]
= 1, 2, ..., 0 < x
is the degrees of freedom
e is the base of the natural logarithm, sometimes called Euler's e (2.71...)
(gamma) is the Gamma function
The above animation shows the shape of the Chi-square distribution as the degrees of freedom increase (1, 2, 5, 10, 25 and 50).
Circumplex. A circumplex is a set of variables that, when plotted as vectors in N-dimensional space, fall in a circular pattern. If a set of variables forms a circumplex, the correlation matrix for the variables will have an unusual pattern called circular structure. In this pattern, the correlations on diagonal strips below the main diagonal tend to be equal, or nearly so, first becoming smaller, then larger again as we move away from the main diagonal. Below is a circular structure for an 8x8 correlation matrix.
City-Block (Manhattan) Distance. A distance measure computed as the average difference across dimensions. In most cases, this distance measure yields results similar to the simple Euclidean distance. However, note that in this measure, the effect of single large differences (outliers) is dampened (since they are not squared). See also, Cluster Analysis.
Classification. Assigning data (i.e., cases or observations) cases to one of a fixed number of possible classes (represented by a nominal output variable).
Classification (in Neural Networks). In classification, the aim is to assign input cases to one of a number of classes. Classification problems fall into two categories: two-class problems, and many-class problems. A two-class problem is usually encoded using a single output neuron. Many-class problems use one output neuron per class. It is also possible to encode two-class problems using this approach (i.e., using two output neurons), and this is in fact the approach taken by PNN networks.
Single output neuron. In these two-class networks, the target output is either 1.0 (indicating membership of one class) or 0.0 (representing membership of the other).
Multiple output neurons. In many-class problems, the target output is 1.0 in the correct class output and 0.0 in the others.
The output neuron activation levels provide confidence estimates for the output classes. It is desirable to be able to interpret these confidence levels as probabilities. If the correct choice of network error function is used during optimization, combined with the correct activation function, such as interpretation can be made. Specifically, a cross entropy error function is combined with the logistic activation function for a two-class problem encoded by a single output neuron or with softmax for a three or more class problem.
The entropic approach corresponds to maximum likelihood optimization, assuming that the data is drawn from the exponential family of distributions. An important feature is that the outputs may be interpreted as posterior estimates of class membership probability.
The alternative approach is to use a sum-squared error function with the logistic output activation function. This has less statistical justification - the network learns a discriminant function, and although the outputs can be treated as confidence measures, they are not probability estimates (and indeed may not even sum to 1.0). On the other hand, such networks sometimes train more quickly, the training process is more stable, and they may achieve higher classification rates.
Classification by Labeled Exemplars (in Neural Networks). Classification neural networks must translate the numeric level on the output neuron(s) to a nominal output variable. There are two very different approaches to assigning classifications. In one of these, the activation level of the output layer units determines the class, usually by interpreting the activation level as a confidence measure, and finding the highest confidence class. That approach is used in most neural network types. This topic discusses the alternative approach, which is used in SOFM and Cluster networks.
These types of networks store labeled exemplar vectors in their radial layer. When a new case is presented to the network, the network in essence calculates the distance between the (possibly normalized) new case and each exemplar vector; the activations of the neurons encode these distances. Each of these neurons has a class label. The class label of the "winning" (smallest distance from input case) neuron is typically used as the output of the network. The standard algorithm is extended slightly using the KL nearest neighbor algorithm; the class assigned by the network is the most common class among the K winning neurons, provided that at least L of them agree (otherwise, the class is "Unknown").
The input case might actually be very distant from any of the exemplar vectors, in which case it may be better to assign the case as "Unknown." We can optionally specify an accept threshold for this eventuality. If the normalized distance is greater than this threshold, the class is "Unknown."
Classification Statistics (in Neural Networks). One of the major uses of neural networks is to perform classification tasks, i.e., to assign cases to one of a number of possible classes. The class of a case is indicated by the use of a nominal output variable.
The classification statistics include, for each class:
Total. Number of cases of that class.
Correct. Number of cases correctly classified.
Wrong. Number of cases erroneously assigned to another class.
Unknown. Number of cases that could not be positively classified.
Correct (%). Percentage of cases correctly classified.
Wrong (%). Percentage of cases wrongly classified.
Unknown (%). Percentage of cases classified as unknown.
Classification Thresholds (in Neural Networks). Classification neural networks must translate the numeric activation level of the output neuron(s) to a nominal output variable. There are two very different approaches to assigning classifications. One of these, where the neural network determines the "winning" neuron or neurons in the radial layer of the network, and then uses the class labels on those neurons, is described in Classification by Labeled Exemplars. This approach is used in SOFM and Cluster networks.
Here we discuss the alternative approach, where it is the activation level of the output layer units, which are not radial units, that determines the class. This approach is used in all other network types.
Two cases need to be distinguished: single output neuron versus multiple output neurons.
Single output neurons are typically used for two-class problems, with a high output neuron level indicating one class and a low activation the other class. This configuration uses the Two-state conversion function.
Multiple output neurons are typically used for three or more class problems. One neuron is used for each class, and the highest activation neuron indicates the class. The neuron activation levels can be interpreted as confidence levels. This method is implemented by using the One-of-N conversion function for the output variable. We can optionally configure a multilayer perceptron to use two output neurons for a two-class output variable.
Single output neuron. Two thresholds are used: accept and reject. In the single output neuron case, the output is considered to be the first class if the output neuron's activation is below the reject threshold, and to be the second class if its activation is above the accept threshold. If the activation is between the two thresholds, the class is regarded as "unknown" (the so-called doubt option). If the two thresholds are equal, there is no doubt option. There are two common configurations: accept=reject=0.5 implies no doubt option, with the most likely class assigned; accept=0.95, reject=0.05 implies standard "95% confidence" in assignment of a class, with doubt expressed otherwise. Both of these cases assume the standard logistic output neuron activation function, which gives the output neuron a (0,1) range; the thresholds should be adjusted accordingly for different activation functions [e.g., hyperbolic tangent uses output range (-1,+1)].
As an alternative to selecting the thresholds ourself, we can specify a loss coefficient that gives the relative "cost" of the two possible misclassifications (false-positive versus false-negative). A loss coefficient of 1.0 indicates that the two classes are equally important. A loss coefficient above 1.0 indicates that it is relatively more important to correctly recognize class two cases, even at the expense of misclassifying more class one cases. The thresholds (which are equal) are determined by calculating a ROC curve and determining the point on the curve where the ratio of false positives to false negatives equals the loss coefficient. This equalizes the weighted loss on each class, independent of the number of cases in each (i.e. with a loss coefficient of 1.0, it is the proportion of misclassifications in each class that is equalized, not the number of absolute number of misclassifications).
Multiple output neurons. If no thresholds are used, the network uses a "winner takes all" algorithm; the highest activation neuron gives the class. There is a "no doubt" option. If we specify thresholds, the class is still assigned to the highest neuron, but there is a doubt option. The highest neuron's activation must be above the accept threshold and all other neuron's below the reject threshold in order for the class to be assigned; if this condition is not fulfilled, the class is "unknown."
If our multiple output neuron classification network is using the softmax activation function, the output neuron activations are guaranteed to sum to 1.0 and can be interpreted as probabilities of class membership. However, in other cases, although the activations can be interpreted as confidence levels in some sense (i.e. a higher number indicates greater confidence), they are not probabilities, and should be interpreted with caution.
Ordinal classification. If we have a large number of classes, one-of-N encoding can become extremely unwieldy, as the number of neurons in the network proliferates. An alternative approach then is to use ordinal encoding. The output is mapped to a single neuron, with the output class represented by the ordinals 1, 2, 3, etc. The problem with this technique is that it falsely implies an ordering on the classes (i.e. class 1 is more like class 2 than class 3). However, in some circumstances it may be the only viable approach.
We can specify ordinal encoding by changing the conversion function of the output variable to minimax. The ordinals are then mapped into the output range of the neuron. With ordinal encoding, the output is determined as follows. The output neuron's activation is linearly scaled using the factors determined by minimax, then rounded to the nearest integer. This ordinal value gives the class.
The only classification threshold used is the accept threshold. If the difference between the output and the selected ordinal is greater than the threshold, then the classification is instead "unknown."
Example. The output is 3.8, which is rounded to the nearest ordinal 4. The difference is 0.2. If an accept threshold less than 0.2 has been selected, the classification is rejected, and "unknown" is generated. An accept threshold of 0.5 or above is equivalent to not using a threshold at all.
Classification Trees. Classification trees are used to predict membership of cases or objects in the classes of a categorical dependent variable from their measurements on one or more predictor variables. For a detailed description of classification trees, see the Classification Trees overview.
Class Labeling (in Neural Networks). A variety of clustering algorithms and networks are supported. All of these have networks where the second layer consists of Radial units, and these units contain exemplar vectors. In SOFM and Cluster networks, these are combined in a two layer neural network with a single nominal output variable and the KNearest output conversion function to produce a classification based upon the nearest exemplar vector(s) to an input case.
The exemplar vectors can be positioned using a variety of cluster-center and sampling approaches. However, it is also necessary to apply class labels to the radial units (i.e. label each radial unit as representative of a particular class). Class labels can be applied using the class labeling algorithms described here.
KL Nearest Neighbor Labeling. This algorithm assigns labels to units based upon the labels of the K nearest neighboring training cases. Provided that at least L of the K neighbors are of the same class, this class is used to label the unit. If not, a blank label is applied, signifying an "unknown" class. Note that this is distinct from (although related to) the KL-Nearest algorithm used when executing cluster networks, which reports the class of at least L of the K nearest units to the input case.
Voronoi Labeling. This algorithm assigns labels to units based upon the labels of the training cases that are "assigned" to that unit. Assigned cases are those that are nearer to this unit than to any other (i.e. those that would be classified by this unit if using the 1-NN classification scheme). These are the cases in the Voronoi neighborhood of the unit. The class of the majority of the training cases is used to label the unit, provided that at least a given minimum proportion of the training cases belong to this majority. If not, a blank label is applied, signifying an "unknown" class.
Cluster Analysis. The term cluster analysis (first used by Tryon, 1939) actually encompasses a number of different classification algorithms that can be used to develop taxonomies (typically as part of exploratory data analysis). For example, biologists have to organize the different species of animals before a meaningful description of the differences between animals is possible. According to the modern system employed in biology, man belongs to the primates, the mammals, the amniotes, the vertebrates, and the animals. Note how in this classification, the higher the level of aggregation the less similar are the members in the respective class. Man has more in common with all other primates (e.g., apes) than it does with the more "distant" members of the mammals (e.g., dogs), etc. For information on specific types of cluster analysis methods, see Joining (Tree Clustering), Two-way Joining (Block Clustering), and k-Means Clustering. See the Cluster Analysis overview for more general information; see also Classification Trees.
Cluster Diagram (in Neural Networks). A scatter diagram plotting cases belonging to various classes in two dimensions. The dimensions are provided by the output levels of units in the neural network. See also, Cluster Analysis.
Cluster Networks (in Neural Networks). Cluster networks are actually a non-neural model presented in a neural form for convenience. A cluster network consists of a number of class-labeled exemplar vectors (each represented by a radial neuron). The vectors are assigned centers by clustering algorithms such as K-Means, and then labeled using nearby cases. After labeling, the centers positions can be fine-tuned using Learned Vector Quantization.
Cluster networks are closely related to Kohonen networks, with a few differences. Cluster networks are intended for supervised learning situations where class labels are available in the training data and cluster networks do not have a topologically organized output layer.
Training consists of center assignment, followed by labeling. This can optionally be followed by LVQ training to improve center location. After training, the network can be pruned, and classification factors set, including an acceptance threshold and K,L factors for KL nearest neighbor classification.
Coarse Coding. The process of dividing values of either continuous or categorical variables into a relatively small number of categories is called coarse coding. This technique is used in credit scoring and the making of credit score cards. It is important that a credit score card be fast and easy to use, and recoding data into a manageable set of categories helps.
Codes. Codes are values of a grouping variable (e.g., 1, 2, 3, ... or MALE, FEMALE) that identify the levels of the grouping variable in an analysis. Codes can either be text values or integer values.
Coefficient of Determination. This is the square of the product-moment correlation between two variables (r²). It expresses the amount of common variation between the two variables. See also, Hays, 1988.
Coefficient of Variation. The coefficient of variation is a relative measure of the dispersion of data points around the mean. Calculated as the ratio of the standard deviation divided by the mean expressed as a percentage, the coefficient of variation can be used to compare the degree of variation from one sample to another, even if the means are different.
where s is the sample standard deviation and is the sample mean.
When the standard deviation and mean come from repeated measurements of a single subject, (e.g., an athlete's performance in different competitions) the resulting coefficient of variation is an important measure of reliability.
Columns (Box Plot). In this type of box plot, vertical columns are used to represents the variable's midpoint (i.e., mean or median). The whiskers superimposed on each column mark the selected range (i.e., standard error, standard deviation, min-max, or constant) around the midpoint.
Communality. In Principal Components and Factor Analysis, communality is the proportion of variance that each item has in common with other items. The proportion of variance that is unique to each item is then the respective item's total variance minus the communality. A common starting point is to use the squared multiple correlation of an item with all other items as an estimate of the communality (refer to Multiple Regression for details about multiple regression). Some authors have suggested various iterative "post- solution improvements" to the initial multiple regression communality estimate; for example, the so-called MINRES method (minimum residual factor method; Harman & Jones, 1966) will try various modifications to the factor loadings with the goal to minimize the residual (unexplained) sums of squares.
Complex Numbers. Complex numbers are the superset that includes all real and imaginary numbers. A complex number is usually represented by the expression a + ib where a and b are real numbers and i is the imaginary part of the expression where i has the property that i**2=-1. See also, Cross-spectrum Analysis in Time Series.
Conditional Probability. In many situations, once more information becomes available, we are able to revise our estimates for the probability of further outcomes or events happening. The probability of the A event given that event B has occurred is known as the conditional probability of A given B and is often denoted by p(A|B).
Confidence Interval. The confidence intervals for specific statistics (e.g., means, or regression lines) give us a range of values around the statistic where the "true" (population) statistic can be expected to be located (with a given level of certainty, see also Elementary Concepts).
For example, the animation above shows a 90%, 95% and 99% confidence interval for the regression line.
Confidence Interval for the Mean. The confidence intervals for the mean give us a range of values around the mean where we expect the "true" (population) mean is located (with a given level of certainty, see also Elementary Concepts). In some statistics or math software packages (e.g., in STATISTICA) we can request confidence intervals for any p-value; for example, if the mean in your sample is 23, and the lower and upper limits of the p=.05 confidence interval are 19 and 27 respectively, then we can conclude that there is a 95% probability that the population mean is greater than 19 and lower than 27. If we set the p-value to a smaller value, then the interval would become wider thereby increasing the "certainty" of the estimate, and vice versa; as we all know from the weather forecast, the more "vague" the prediction (i.e., wider the confidence interval), the more likely it will materialize. Note that the width of the confidence interval depends on the sample size and on the variation of data values. The calculation of confidence intervals is based on the assumption that the variable is normally distributed in the population. This estimate may not be valid if this assumption is not met, unless the sample size is large, say n = 100 or more.
Confidence Interval vs. Prediction Interval. In regression, it is possible to predict the value of the dependent variable based on given values of the independent variables. When these values are predicted, it is also possible to calculate confidence intervals and/or prediction intervals for the dependent variable.
The confidence interval gives information on the expected value (mean) of the dependent variable. That is, a confidence interval for a predicted value of the dependent variable gives a range of values around which the "true" (population) mean (of the dependent variable for given levels of the independent variables) can be expected to be located (with a given level of certainty, see also Elementary Concepts).
The prediction interval gives information on individual predictions of the dependent variable. That is, a prediction interval for a predicted value of the dependent variable gives us a range of values around which an additional observation of the dependent variable can be expected to be located (with a given level of certainty, see also Elementary Concepts).
Note that the confidence interval will produce a smaller range of values, because it is an interval estimate for an average rather than an interval estimate for a single observation. See Neter, Wasserman, & Kutner, 1985.
Confidence Limits. The same as Confidence Intervals. In Neural Networks, they represent the accept and reject thresholds, used in classification tasks, to determine whether a pattern of outputs corresponds to a particular class or not. These are applied according to the conversion function of the output variable (One-of-N, Two-state, Kohonen, etc).
Confidence Value (Association Rules). When applying (in data or text mining) algorithms for deriving association rules of the general form If Body then Head (e.g., If (Car=Porsche and Age<20) then (Risk=High and Insurance=High)), the Confidence value denotes the conditional probability of the Head of the association rule, given the Body of the association rule.
Confusion Matrix (in Neural Networks). A name sometimes given to a matrix, in a classification problem, displaying the numbers of cases actually belonging to each class, and assigned by the neural network to that or other classes. Displayed as classification statistics in STATISTICA Neural Networks.
Conjugate Gradient Descent (in Neural Networks). Conjugate gradient descent (Bishop, 1995; Shepherd, 1997) is an advanced method of training multilayer perceptrons. It usually performs significantly better than back propagation, and can be used wherever back propagation can be. It is the recommended technique for any network with a large number of weights (more than a few hundred) and/or multiple output units. For smaller networks, either Quasi-Newton or Levenberg-Marquardt may be better, the latter being preferred for low-residual regression problems.
Conjugate gradient descent is a batch update algorithm: whereas back propagation adjusts the network weights after each case, conjugate gradient descent works out the average gradient of the error surface across all cases before updating the weights once at the end of the epoch.
For this reason, there is no shuffle option available with conjugate gradient descent, since it would clearly serve no useful function. There is also no need to select learning or momentum rates for conjugate gradient descent, so it can be much easier to use than back propagation. Additive noise would destroy the assumptions made by conjugate gradient descent about the shape of search space, and so is also not available.
Conjugate gradient descent works by constructing a series of line searches across the error surface. It first works out the direction of steepest descent, just as back propagation would do. However, instead of taking a step proportional to a learning rate, conjugate gradient descent projects a straight line in that direction and then locates a minimum along this line, a process that is quite fast as it only involves searching in one dimension. Subsequently, further line searches are conducted (one per epoch). The directions of the line searches (the conjugate directions) are chosen to try to ensure that the directions that have already been minimized stay minimized (contrary to intuition, this does not mean following the line of steepest descent each time).
The conjugate directions are actually calculated on the assumption that the error surface is quadratic, which is not generally the case. However, it is a fair working assumption, and if the algorithm discovers that the current line search direction isn't actually downhill, it simply calculates the line of steepest descent and restarts the search in that direction. Once a point close to a minimum is found, the quadratic assumption holds true and the minimum can be located very quickly.
Note: The line searches on each epoch of conjugate gradient descent actually involve one gradient calculation plus a variable number (perhaps as high as twenty) of error evaluations. Thus a conjugate gradient descent epoch is substantially more time-consuming (typically 3-10 times longer) than a back propagation epoch. If we want to compare performance of the two algorithms, we will need to record the time taken.
Technical Details. Conjugate gradient descent is batch-based; it calculates the error gradient as the sum of the error gradients on each training case.
The initial search direction is given by:
Subsequently, the search direction is updated using the Polak-Rebiere formula:
If the search direction is not downhill, the algorithm restarts using the line of steepest descent. It restarts anyway after W directions (where W is the number of weights), as at that point the conjugacy has been exhausted.
Line searches are conducted using Brent's iterative line search procedure, which utilizes a parabolic interpolation to locate the line minima extremely quickly.
Continuous Dependent Variable. A continuous dependent variable is a variable of interest (a researcher wants to predict) measured on a continuous scale (such as Weight in pounds, or Height in centimeters). A researcher may be interested in predicting a continuous variable (e.g., Annual Sales) based on one or more predictors (e.g., based on Marketing Expenditures).
Contour/Discrete Raw Data Plot. This sequential plot can be considered to be a 2D projection of the 3D Ribbons plot. Each data point in this plot is represented as a rectangular region, with different colors and/or patterns corresponding to the values (or range of values of the data points (the ranges are described in the legend). Values within each series are presented along the X-axis, with each series plotted along the Y-axis.
Contour Plot. A contour plot is the projection of a 3-dimensional surface onto a 2-dimensional plane.
As compared to surface plots, they may be less effective to quickly visualize the overall shape of 3D data structures,
however, their main advantage is that they allow for precise examination and analysis of the shape of the surface (Contour Plots display a series of undistorted horizontal "cross sections" of the surface).
Cook's Distance. Another measure of impact of the respective case on the regression equation. It indicates the difference between the computed B values and the values we would have obtained, had the respective case been excluded. All distances should be of about equal magnitude; if not, then there is reason to believe that the respective case(s) biased the estimation of the regression coefficients. See also, standard residual value, Mahalanobis distance, and deleted residual.
Correlation. Correlation is a measure of the relation between two or more variables. Correlation coefficients can range from -1.00 to +1.00. The value of -1.00 represents a perfect negative correlation while a value of +1.00 represents a perfect positive correlation. A value of 0.00 represents a lack of correlation. See also, Correlation, Partial Correlation, Pearson Correlation and Spurious Correlations.
Correlation Value (Association Rules). When applying (in data or text mining) algorithms for deriving association rules of the general form If Body then Head (e.g., If (Car=Porsche and Age<20) then (Risk=High and Insurance=High)), a Correlation value can be computed as the support value for the rule, divided by the square root of the product of the support values for the Body and Head computed separately.
Correspondence Analysis. Correspondence analysis is a descriptive/exploratory technique designed to analyze simple two-way and multi-way tables containing some measure of correspondence between the rows and columns. The results provide information that is similar in nature to those produced by Factor Analysis techniques, and they allow us to explore the structure of categorical variables included in the table. The most common kind of table of this type is the two-way frequency crosstabulation table (see, for example, Basic Statistics or Log-Linear). See the Correspondence Analysis overview for more information.
Cox-Snell Generalized Coefficient of Determination. A generalization to the coefficient of determination found in linear models is the statistic proposed by Cox and Snell:
Where L0 is the likelihood of the intercept only model and Lβ is the likelihood for the specified model, and n is the number of observations.
Cpk, Cp, Cr.
Potential capability (Cp). This is the simplest and most straightforward indicator of process capability. It is defined as the ratio of the specification range to the process range; using ± 3 sigma limits we can express this index as:
Cp = (USL-LSL)/(6*Sigma)
Put into words, this ratio expresses the proportion of the range of the normal curve that falls within the engineering specification limits (provided that the mean is on target, that is, that the process is centered).
Non-centering correction (K). We can correct Cp for the effects of non-centering. Specifically, we can compute:
K = abs(Target Specification - Mean)/(1/2(USL-LSL))
This correction factor expresses the non-centering (target specification minus mean) relative to the specification range. Demonstrated excellence (Cpk). Finally, we can adjust Cp for the effect of non-centering by computing:
Cpk = (1-k)*Cp
If the process is perfectly centered, then k is equal to zero, and Cpk is equal to Cp. However, as the process drifts from the target specification, k increases and Cpk becomes smaller than Cp.
Capability ratio (Cr). This index is equivalent to Cp; specifically, it is computed as 1/Cp (the inverse of Cp).
Estimate of sigma. When the data set consists of multiple samples, such as data collected for the quality control chart, then we can compute two different indices of variability in the data. One is the regular standard deviation for all observations, ignoring the fact that the data consist of multiple samples; the other is to estimate the process's inherent variation from the within-sample variability. When the total process variability is used in the standard capability computations, the resulting indices are usually referred to as process performance indices (as they describe the actual performance of the process; common indices are Pp, Pr, and Ppk), while indices computed from the inherent variation (within-sample sigma) are referred to as capability indices (since they describe the inherent capability of the process; common indices are Cp, Cr, and Cpk).
See Process Capability Indices and Process Capability Analysis.
CRISP. See Models for Data Mining. See also, Data Mining Techniques.
Cross Entropy (in Neural Networks). Error functions based on information-theoretic measures, and particularly appropriate for classification networks. There are two versions, for single-output networks and multiple-output networks; these should be combined with the logistic and softmax activation functions respectively (Bishop, 1995). See also, Neural Networks.
Cross Verification (in Neural Networks). The same as Cross-Validation. In the context of neural networks, the use of an auxiliary set of data (the verification set) during iterative training. While the training set is used to adjust the network weights, the verification set maintains an independent check that the neural network is learning to generalize.
Cross-Validation. Cross-validation refers to the process of assessing the predictive accuracy of a model in a test sample (sometimes also called a cross-validation sample) relative to its predictive accuracy in the learning sample from which the model was developed. Ideally, with a large sample size, a proportion of the cases (perhaps one-half or two-thirds) can be designated as belonging to the learning sample and the remaining cases can be designated as belonging to the test sample. The model can be developed using the cases in the learning sample, and its predictive accuracy can be assessed using the cases in the test sample. If the model performs as well in the test sample as in the learning sample, it is said to cross-validate well, or simply to cross-validate. For discussions of this type of test sample cross-validation, see the Computational Methods section of Classification Trees, the Classification section of Discriminant Analysis, and Data Mining.
A variety of techniques have been developed for performing cross-validation with small sample sizes by constructing test samples and learning samples that are partly but not wholly independent. For a discussion of some of these techniques, see the Computational Methods section of Classification Trees.
Crossed Factors. Some experimental designs are completely crossed (factorial designs), that is, each level of each factor appears with each level of all others. For example, in a 2 (types of drug) x 2 (types of virus) design, each type of drug would be used with each type of virus. See also, ANOVA/MANOVA.
Crosstabulations (Tables, Multi-way Tables). A crosstabulation table is a combination of two (or more) frequency tables arranged such that each cell in the resulting table represents a unique combination of specific values of crosstabulated variables. Thus, crosstabulation allows us to examine frequencies of observations that belong to specific combinations of categories on more than one variable. For example, the following simple ("two-way") table shows how many adults vs. children selected "cookie A" vs. "cookie B" in a taste preference test:
By examining these frequencies, we can identify relations between crosstabulated variables (e.g., children clearly prefer "cookie B"). Only categorical (nominal) variables or variables with a relatively small number of different meaningful values should be crosstabulated. Note that in the cases where we do want to include a continuous variable in a crosstabulation (e.g., income), we can first recode it into a particular number of distinct ranges (e.g., low, medium, high). For more information, see the section on Crosstabulations in Basic Statistics.
C-SVM Classification. A Support Vector Machine implementation for solving multi-classification prediction problems.
Cubic Spline Smoother. The cubic spline scatterplot smoother is a special smoothing technique for 2D scatterplots, which generally produces a smooth generalization of the relationship between the two variables in the scatterplot. The cubic spline smoother is often used in generalized additive models, to estimate the unspecific (non-parametric) function of the predictor variables that best predicts the (transformed) dependent variable values. Computational details regarding the cubic spline smoother, and comparisons to other smoothing algorithms, can be found in Hastie and Tibshirani, 1990, and Schimek, 2000.
"Curse" of Dimensionality. The term curse of dimensionality (Bellman, 1961, Bishop, 1995) generally refers to the difficulties involved in fitting models, estimating parameters, or optimizing a function in many dimensions, usually in the context of neural networks. As the dimensionality of the input data space (i.e., the number of predictors) increases, it becomes exponentially more difficult to find global optima for the parameter space, i.e., to fit models. In practice, the complexity of neural networks becomes unmanageable when the number of inputs into the neural network exceeds a few hundreds or even less, depending on the complexity of the respective neural network architecture. Hence, it is simply a practical necessity to pre-screen and preselect from among a large set of input (predictor) variables those that are of likely utility for predicting the outputs (dependent variables) of interest. See also, Feature Selection.