By STEVE LOHR

A TOUR of its carefully tended, 300-acre corporate campus here leaves little doubt why surveys, year after year, rate the SAS Institute, the world’s largest private software company, among the best places to work.

There is the subsidized day care and preschool. There are the four company doctors and the dozen nurses who provide free primary care. The recreational amenities include basketball and racquetball courts, a swimming pool, exercise rooms and 40 miles of running and biking trails. There is a meditation garden, as well as on-site haircuts, manicures, and jewelry repair. Employees are encouraged to work 35-hour weeks.

Academics have studied the company’s benefit-enhanced corporate culture as a model for nurturing creativity and loyalty among engineers and other workers. Six years ago, in a report on “60 Minutes,” Morley Safer called working at SAS “the good life.”

But that good life is under threat today as never before. SAS’s specialty, a lucrative niche called business intelligence software, is becoming mainstream. Free, open-source alternatives to some of the company’s products are increasingly popular. On the other end of the spectrum, the heavyweights of the software industry — Oracle, SAP, Microsoft and, especially, I.B.M. — are plunging in and investing billions of dollars.

“It will be a dogfight,” says Bill Hostmann, an analyst at Gartner. “SAS has never faced a competitor like I.B.M. And I do think I.B.M. sees SAS as a big, fatted cow.”

The term “business intelligence software” applies to a wide range of products and services, but all the technology is aimed at helping businesses mine nuggets of insight from mountains of data. SAS has traditionally specialized in advanced software to analyze huge data sets and to generate predictive statistical models for large corporations and government agencies.

Credit card companies, for example, use SAS to detect unusual buying patterns in real time, and to spot potentially fraudulent charges. Giant retail chains use SAS to tailor pricing and product offerings down to the store level. Telecommunications companies use SAS to identify the few thousand customers, among millions, most likely to switch to another cellphone carrier, and to aim marketing at them. SAS software is also used to parse sensor signals from North Sea oil rigs, combined with weather and structural data, to predict failure of parts before it happens. Of the 100 largest companies worldwide, 92 use SAS software.

But as the stream of companies’ collected data turns into a torrent, SAS and other software companies are trying to find new ways to harness it. The information is generated not only by computerized systems for tracking operations, customers and sales. It also comes from new data sources like Web site visits, social network chatter and public records accessible over the Internet, as well as genome sequences, sensor signals and surveillance tapes, all in digital form.

This data explosion, experts say, is an untapped asset at most companies, which lack the tools and skills to exploit it. Yet the long-range potential, they say, is to use this data for far more fine-grained analysis of markets, customer behavior and operations, making business more of a science and less a seat-of-the-pants art.

“Now, the data is available so business can move toward evidence-based decision-making,” says Erik Brynjolfsson, an economist and director of the Center for Digital Business at the Massachusetts Institute of Technology. “This market is a huge opportunity.”

That opportunity is not lost on SAS. “Our advantage is the incredible depth of our technology, developed over years and applied to specific industries,” says James H. Goodnight, the chief executive and a co-founder of SAS. “No one can match our toolbox.”

Indeed, no one underestimates SAS’s technical prowess. The big question is whether the company’s seemingly pampered culture can embrace the higher-octane institutional metabolism that it will need to succeed.

“We know we have to change — no question about it,” says Jim Davis, 51, a senior vice president at SAS. “Our market space has changed dramatically in the last 18 months or so, more than at any time over the 33-year history of the company. We can’t sit back. Things are only going to get faster.”

THE company traces its roots to a time when computing was costly and for the few. Originally called Statistical Analysis System, it was founded in 1976 by Mr. Goodnight and three colleagues from the agricultural statistics department at North Carolina State University. Its techniques were initially used to calculate the intricacies of soil, weather, seed varieties and other factors to improve crop yields.

To build an audience, Mr. Goodnight spent nights packing up boxes of computer tapes and manuals, which he sent to university and corporate researchers. Soon, companies wanted him and his academic colleagues to develop software tools tailored for industry. In 1976 at a users’ conference, 300 or so people showed up, many from business.

“That was pretty much an ‘aha’ moment for us, that it was time to expand beyond the university,” Mr. Goodnight recalls. “It was a little scary, cutting the academic umbilical cord. But I was convinced we could do it.”

He and his colleagues at SAS developed their own programming language and software tools, and designed them for eggheads like themselves. Users were analysts with Ph.D.’s, working with programmers and employed by the largest companies at the forefront of using computing in their businesses, including banks, national retailers, insurers and drug companies.

SAS invested heavily in research and development, and even today allocates 22 percent of the company’s revenue to research. The formula has paid off in steady growth, year after year. Revenue reached $2.26 billion in 2008, up from $1.34 billion five years earlier.

Yet the company also faces the classic challenge of being the innovative pioneer — enjoying rich profit margins but facing new competition from rivals seeking to gain market share with lower prices and substitute technology.

In the last two years, the major software companies have scooped up companies in the business intelligence market. Among the larger moves, SAP bought Business Objects for $6.8 billion, I.B.M. bought Cognos for $4.9 billion and Oracle picked up Hyperion for $3.3 billion.

Still, those companies compete in the broad swath of the business intelligence market for reporting and analysis products. Such data on sales, shipments, customers and operations amount to a numbers-laden portrait of the recent past. The SAS stronghold is a more sophisticated kind of software typically called “advanced analytics and predictive modeling,” which uses historical and current data to try to peer into the future and model likely outcomes.

The competitive thrust that really grabbed SAS’s attention came in late July, when I.B.M. announced that it planned to pay $1.2 billion for SPSS, a maker of predictive modeling software. I.B.M. has placed SPSS and Cognos into a new business analytics and optimization group. That business will be supported by 200 scientists, and the company has said it will retrain or hire 4,000 consultants and analysts to work in the group.

“This is the big growth strategy for I.B.M., the company’s next big play for this decade,” says Ambuj Goyal, a computer scientist who is general manager of I.B.M’s business analytics software unit. “SAS comes from the legacy world of statisticians and programmers. The real opportunity is in deploying this technology broadly in corporations.”

To counter I.B.M. and others, SAS is looking to forge a tighter relationship with a big technology services company. It is also shortening product development cycles to 12 to 18 months, down from 24 to 36. “That’s what the market expects,” Mr. Davis says.

The most sweeping change is the company’s move toward the Internet model of software delivery — as a service that customers tap into over the Web, much as Google and other Internet companies do. SAS has dipped its toe in, with some initial products. But a major expansion is planned, supported by a sprawling $70 million data center scheduled to begin operating next year.

The remotely delivered software is part of a drive to broaden the market for SAS technology beyond an elite corps of quantitative analysts and into the rank-and-file of corporate professionals.

Analysts say the company’s strategy looks sound, even if the outcome is uncertain. “SAS has to do a lot of things right to succeed,” says Peter Sondergaard, senior vice president of research for Gartner. “But if it executes correctly, it could be a winner.”

ACROSS its campus here, there are signs that the SAS culture is evolving with the times. Rick Langston, 54, a senior software manager who joined the company 29 years ago, smiles and shrugs when asked about the 35-hour workweek. After leaving the office, Mr. Langston routinely checks on work e-mail at home.

These days, he explains, SAS is a global company with far-flung project teams, and overnight e-mails can resolve problems and speed things along. Deadline work to meet product development schedules, he adds, can mean long hours at times. “But this is certainly not a place where you are working 60-hour weeks, week in and week out,” he said.

To be sure, the corporate cocoon in Cary can breed insularity. SAS, for example, was slow to recognize the brewing challenge from free, open-source alternatives to some of its products. A free programming language and set of software tools for statistical computing, called R, has become increasingly popular at universities and labs.

The company shifted course earlier this year and modified its software so programs written with R work seamlessly with SAS technology. “Shame on us for not engaging more with the open-source community,” says Keith Collins, senior vice president and chief technology officer. “But we’re committed to doing that now.”

THE architect of the SAS culture is Mr. Goodnight, a lanky, laconic billionaire. The benefits have built up gradually over the years as a series of pragmatic steps, he says. The day-care program began after a valued employee was about to leave to take care of her young child. The on-site medical checkups grow out of the belief that “good health is good business,” he says.

Today, SAS estimates that its health care center saves the company $5 million a year, by providing care more cheaply than an outside insurer and by not having employees leave the campus for doctor’s visits. Employee turnover at SAS averages 4 percent a year, versus about 20 percent for the overall software industry.

The office atmosphere is sedate. There are no dogs roaming the halls, no Nerf-ball fights, no one jumping on trampolines — no whiff of Silicon Valley. The SAS culture is engineered for its own logic: to reduce distractions and stress, and thus foster creativity.

“The SAS model is sensible and durable; there’s nothing faddish or ephemeral,” says Richard Florida, a professor at the Rotman School of Management at the University of Toronto, who has studied SAS and is the author of “The Rise of the Creative Class.”

During the technology boom at the start of this decade, SAS considered a drastic change in its model: going public. Goldman Sachs bankers were brought in as advisers, and in 2000 SAS recruited a former Oracle executive, Andre Boisvert, as its president.

Under Mr. Boisvert, SAS installed a new financial reporting system and paid the sales force incentive commissions rather than salary only. But when technology stocks plummeted, the appeal of selling shares to the public also receded. Mr. Boisvert resigned from SAS in 2001 and is now an independent investor and consultant.

Mr. Goodnight recalls those days as a brief period of New Economy surrealism, and going public as a path wisely avoided. SAS, he says, is a culture averse to the short-term pressures of Wall Street, which he characterizes as “a bunch of 28-year-olds, hunched over spreadsheets, trying to tell you how to run your business.”

Unlike many other tech companies, SAS has had no recession-related layoffs this year. “I’ve got a two-year pipeline of projects in R & D,” Mr. Goodnight says. “Why would I lay anyone off?”

Mr. Goodnight, though 66, has no plans to retire himself. His fingerprints, colleagues say, remain all over the business, especially in meeting with customers and in overseeing research.

He is not only a statistician, but also a bit of gambler who enjoys calculating his chances. For example, he is co-author of a paper that simulated millions of possible outcomes in blackjack.

Mr. Goodnight regards his new rivals the way a confident card player might. He likes the odds, and he likes his hand.

“We’re pushing as fast as we can to stay ahead — on the cutting edge of everything,” he says. “We’ll do fine.”

Stupid SPSS

Try adding up the values on the Frequency field and see if it matches the total

. .

Mohit Sewak

(http://mohit.sewak.in) , mohit@sewak.in

.

The Changing Consumer Perception

India is poised to become a major Auto hub in the near future. Indian car industry is changing rapidly, so is the mindset of Indian Consumers. We, at the Great Lakes Institute of Management, took an initiative to find out that whether the changing ground realities have also changed the India Auto Consumer’s mindset vis-a-vis their perception of the abilities of various Indian and foreign Auto manufacture to deliver the much sought after attributes in a car. We deliberately, instead of taking individual cars, took BRANDS (as we wanted to analyze the brand perception mapping), and let the consumer decide which brand will he buy (Note: It is important to note that in some cases, though a consumer may covet a brand highly e.g. BMW, but might not intend buying it due to many reasons. So we specifically framed question to analyze the purchase intention), and the attributes for which he will go for that particular brand. It was surprising to find the changing perception of the consumer towards TATA, especially after it being the proud owner of Jaguar and LR on one hand, and the maker of the worlds smallest, and the most economical (& affordable) car Nano on the other. There were many more surprising results as well, have a look… . .

The Result (Survey Dated 8th Decemper, 2009)

.

Attribute Mileage:

Highest: MARUTI- Car people would like to buy the most for its fuel efficiency (eg. NANO).

LOWEST: FORD     – Car people are least like to buy it were for its fuel efficiency (mileage).

Attribute Safety:

Highest: TATA-  Car people would like to buy the most for its Safety preparedness.

LOWEST: FORD- Car people are least like to buy it were for its Safety preparedness.

Attribute Cost (Effectiveness):

Highest: TATA-   Car people would like to buy the most for its cost effectiveness (eg. NANO).

LOWEST: FORD- Car people are least like to buy it were for its cost effectiveness (least value for money).

Attribute Comfort:

Highest: FORD-         Car people would like to buy the most for its Comfort (e.g. Jaguar).

LOWEST: MARUTI- Car people are least like to buy it were for its Comfort.

Attribute After Sales Service (Maintenance cost, frequency and accessibility):

Highest: MARUTI- Car people would like to buy the most for its After Sales Service.

LOWEST: TATA-     Car people are least like to buy it were for its After Sales Service.

.

The Process

.

Cars Analyzed

(Survey Question: Which Brand of car will you choose?)

1. Tata
2. Honda
3. Maruti
4. Ford
5. Hyundai

Attributes Surveyed

(Survey Question: What factor motivates you to buy this brand?)

1. Comfort
2. Safety
3. Mileage
4. Cost (Price)
5. After Sales Service

Respondents

Number: 114 Age Group: 24 to 35 Education: Graduation and above Profession: Management Students Ethnicity: Indians- Representing all states of India Social Class: Middle class and above

Analysis Carried Out

• Statistical Tool: Correspondence Analysis
• Mapping Dimensions: 2
• Test: Chi Square
• Test Value: 73.897
• Test Significance: 0.000 . .

.

Available Downloads:

SPSS Output File For the Data Analysis.

.

By: Mohit Sewak, http://mohit.sewak.in

.

. .

Mohit Sewak

(http://mohit.sewak.in) , mohit@sewak.in

.

The Changing Consumer Perception

.India is poised to become a major Auto hub in the near future. Indian car industry is changing rapidly, so is the mindset of Indian Consumers. We, at the Great Lakes Institute of Management, took an initiative to find out that whether the changing ground realities have also changed the India Auto Consumer’s mindset vis-a-vis their perception of the abilities of various Indian and foreign Auto manufacture to deliver the much sought after attributes in a car. We deliberately, instead of taking individual cars, took BRANDS (as we wanted to analyse the brand perception mapping), and let the consumer decide which brand will he buy (Note: It is important to note that in some cases, though a consumer may covet a brand highly e.g. BMW, but might not intend buying it due to many reasons. So we specifically framed question to analyse the purchase intention), and the attributes for which he will go for that particular brand. It was surprising to find the changing perception of the consumer towards TATA, especially after it being the proud owner of Jaguar and LR on one hand, and the maker of the worlds smallest, and economical (& affordable) car Nano on the other. There were many more surprising results as well, have a look… . .

The Result (Survey Dated 8th Decemper, 2009)

.

Attribute Mileage:

Highest: TATA- Car people would like to buy the most for its fuel efficiency (eg. NANO). LOWEST: HONDA- Car people are least like to buy it were for its fuel efficiency (mileage).

Attribute Safety:

Highest: MARUTI- Car people would like to buy the most for its Safety preparedness. LOWEST: TATA- Car people are least like to buy it were for its Safety preparedness.

Attribute Cost:

Highest: TATA- Car people would like to buy the most for its cost effectiveness (eg. NANO). LOWEST: FORD- Car people are least like to buy it were for its cost (value for money).

Attribute Comfort:

Highest: TATA- Car people would like to buy the most for its Comfort (e.g. Jaguar). LOWEST: HONDA- Car people are least like to buy it were for its Comfort.

Attribute After Sales Service (Maintenance cost, frequency and accessibility):

Highest: TATA- Car people would like to buy the most for its After Sales Service. LOWEST: HONDA- Car people are least like to buy it were for its After Sales Service.

.

The Process

.

Cars Analyzed

(Survey Question: Which Brand of car will you choose?)

1. Tata
2. Honda
3. Maruti
4. Ford
5. Hyundai

Attributes Surveyed

(Survey Question: What factor motivates you to buy this brand?)

1. Comfort
2. Safety
3. Mileage
4. Cost (Price)
5. After Sales Service

Respondents

Number: 114 Age Group: 24 to 35 Education: Graduation and above Profession: Management Students Ethnicity: Indians- Representing all states of India Social Class: Middle class and above

Analysis Carried Out

Statistical Tool: Correspondence Analysis Mapping Dimensions: 2 Test: Chi Square Test Value: 73.897 Test Significance: 0.000 . .

.

Downloads:

1. SPSS Data File For the Research.

2. SPSS Output File For the Data Analysis.

.

By: Mohit Sewak,  http://mohit.sewak.in

.

SPSS програмыг хэрэглэх нь – 4

Өгөгдлийг татаж авсны дараа, эсвэл өгөгдлийг компьютерт оруулж бэлэн болсны дараа бид хувьсагчийг тодорхойлох үйлдлийг гүйцэтгэнэ. Үүнийг Define Variable командаар гүйцэтгэдэг. SPSS-10.0, SPSS-11.0 болон түүнээс хойшхи хувилбаруудад Data Editor цонх нь хоёр горимтой байхаар зохион байгуулагдсан байдаг. Дэлгэцийн доод хэсэгт байх Data view, Variable view гэсэн бичиг бүхий (EXCEL -ийн sheet -тэй адил) хоёр товчийг дарснаар нэг горимоос нөгөө горимд (нэг дэлгэцээс нөгөө дэлгэцэнд) шилжиж болно. Variable view горимд хувьсагчийг тодорхойлох үйлдлийг гүйцэтгэхийн зэрэгцээ хувьсагч тус бүрийн талаар тодорхой мэдээллийг эндээс харж болно.

Хувьсагчийг тодорхойлох үйлдлийг гүйцэтгэхдээ хамгийн түрүүнд хувьсагчийн нэрийг (Name) тодорхойлно. SPSS програмд хувьсагчийн нэрийг тодорхойлохдоо баримтлах зарим нэг журмыг мэдэхэд илүүдэхгүй. (SPSS.15 болон түүнээс хойшхи хувилбаруудад энэ хязгаарлалт багасаж, боломж нь нэмэгдсэн байдаг. Энэ зөвлөмж нь SPSS 11.5 болон SPSS 12 зэрэг өмнөх хувилбаруудад зориулагдсан гэдгийг анхаарна уу?)

• Хувсагчийн нэр наймаас илүүгүй тэмдэгтээс бүрддэг. (Yсэг, тоо, символ) Тиймээс та найман тэмдэгтэд багтааж ойлгомжтой нэр өгөх хэрэгтэй.
• Хувьсагч тус бүр өөр хоорондоо ялгаатайгаар нэрлэгдэх ёстой. Хэрэв хоёр өөр багана буюу хоёр өөр хувьсагч яг адилхан нэртэй байвал хоёрдахь хувьсагч нь VAR00001 гэх мэтчилэн өөр ерөнхий нэрээр нэрлэгдэж, дугаарлагдана.
• Мөн SPSS програмд хувьсагчийг нэрлэхдээ ашиглаж болохооргүй тэмдэгтүүдийг та ашигласан бол нэгэн адил VAR00002 гэх мэтчилэн автоматаар нэрлэгдэнэ. Хэрэв ийм ерөнхий нэр дугаартай хувьсагчид олон гарвал тухайн багананд ямар хувьсагч байгааг ойлгоход хүндрэл гарч будилж болзошгүй. Хувьсагчийн нэрэнд @, #, $, _ зэрэг тэмдэгтүүдийг ашиглаж болно.
• ?, !, -, *, / гэх мэт дээрхээс бусад тэмдэгтүүдийг ашиглах, мөн хувьсагчийн нэр бичихдээ зай (space) оролцуулж болохгүйг анхаар. Эдгээр тэмдэгтүүд нь өөр өөрийн гэсэн үүрэгтэйгээр, өргөн ашиглагддаг тэмдэгтүүд учир хувьсагчийн нэр бичихэд ашиглах боломжгүй байдаг.
• Стандарт функцүүд болон түлхүүр үгнүүдийг хувьсагчийн нэр болгон ашиглаж болохгүй нь ойлгомжтой.
• Хувьсагчийн нэр заавал үсгээр эхэлсэн байх ёстой.

Иймээс та Excell болон өөр бусад ямар нэгэн програм ашиглан мэдээллээ шивэх гэж байгаа бол анхлан толгойгоо оруулахдаа л үүнийг санаж байх хэрэгтэй.

Variable View горимд цонхны бүтэц дараах байдалтай харагдана.

Хувьсагчийн нэр наймаас илүүгүй тэмдэгтээс бүрддэг учраас зарим тохиолдолд шууд хараад тухайн хувьсагчийг ялгах боломжгүй байж болно. Ийм тохиолдолд хувьсагчийн талаарх илүү дэлгэрэнгүй мэдээлэл тайлбарыг бичиж болох Label гэсэн баганыг ашигладаг. Label буюу шошго гэсэн багана нь тухайн хувьсагчийг нэмж тодорхойлсон нэмэлт мэдээллийг агуулна. Практикт энэ талбарт ихэвчлэн анкетийн асуултыг бичдэг юм.

Type гэсэн багананд хувьсагчийн төрлийг тодорхойлно. String буюу үсэгт хувьсагчаас бусад хувьсагчид тоон төрлийн хувьсагчид байна.

Numeric – Үндсэн хэлбэрийн тоон хувьсагч

Comma – тоон хувьсагчийн нэг хэлбэр, тоог гурав гурван орноор тасалж зайгаар тусгаарлана

Dot – тоон хувьсагчийн нэг хэлбэр, тоог гурав гурван орноор тусгаарлаж цэгээр тусгаарлана.

Scientific notation – тоон хувьсагчийн нэг хэлбэр, тоог аравтын зэргээр илэрхийлнэ.

Date – Огноо. Он сар өдрийг олон төрлийн форматаас сонгож хүссэн хэлбэрээрээ бичиж болно

Dollar – Доллар, мөнгөний нэгжийг илэрхийлэхэд ашигладаг.

Custom currency–Бусад мөнгөний нэгж

String – Yсэгт хувьсагч

Data view горимд мэдээллээ оруулах, шалгах засварлах боломжтой.

Variable view горимд тодорхойлсон хувьсагчид маань Data view горимд дараах байдалтайгаар харагддаг.

Мөрийн дагуу тухайн нэг анкетын /тухайн тохиолдолд анкет гэе/ өгөгдлүүд, баганын дагуу хувьсах хэмжигдэхүүнүүд байрлана. Өөрөөр хэлбэл нэг респондентын хариулт буюу нэг анкетын өгөгдлүүд бие даасан нэг мөр болно. Үүнийг case буюу бичлэг (тохиолдол гэх нь ч бий) хэмээн нэрлэнэ. Харин нэг багананд тодорхой нэг шинж тэмдгийн утгууд бичигддэг.

Width гэсэн талбарт зааж өгсөн тоо нь хувьсагчийн өргөнийг тодорхойлно. Өөрөөр хэлбэл Data view горимд тухайн хувьсагч агуулсан багана хэдэн тэмдэгтээс хэтрэхгүй байж болохыг заадаг. Гэхдээ энэ нь үсэгт (string) хувьсагч буюу чанарын шинж тэмдэгт л голлон зориулагдсан байна. Харин тоон хувьсагчид (Numeric болон бусад), хэрэв зайлшгүй тохиолдол биш л бол ихэвчлэн 8 гэсэн стандарт өргөнтэйгөөр тодорхойлогдсон байна.

Decimals – аравтын бутархайн таслалаас хойшхи орны тоог заана. Тоон шинж тэмдгийг хэдэн орны нарийвчлалтайгаар хэмжсэн болон бодолт боловсруулалтыг хэр зэрэг нарийвчлалтайгаар гүйцэтгэх шаардлагатайгаас шалтгаалан зууны нарийвчлалтай юм уу мянганы нарийвчлалтай гэх мэтээр хүссэнээрээ тодорхойлж болно. Социологийн судалгааны мэдээлэл нь голлон чанарын шинж тэмдэг байдаг бөгөөд түүн дотроо нэрлэсэн болон эрэмбийн шкалаар хэмжигдсэн шинж тэмдгүүд дийлэнх байдаг. Тэдгээрийг компьютерт оруулахдаа бүхэл тоогоор тэмдэглэн оруулдаг учраас практикт аравтын бутархайн таслалаас хойшхи орны тоог 0 гэж зааж өгөх нь элбэг. Ингэх нь их хэмжээний мэдээлэлтэй ажиллахад хялбар байх тал бий. Гэхдээ энэ нь Decimals талбарыг дандаа 0 гэж зааж өгөх хэрэгтэй гэсэн үг биш нь ойлгомжтой.

Values талбарт хувьсагчийн авах утгуудыг тодорхойлно.

Жишээлбэл Хүйс гэсэн мэдээллийг оруулахдаа эрэгтэй бол 1, эмэгтэй бол 2 гэсэн тоог оруулсан гэж саная. Value label гэсэн цонхонд тухайлан 1 гэсэн тоогоор эрэгтэйчүүдийг тэмдэглэсэн шүү гэдгийг зааж тодорхойлдог. Ингэхдээ Value гэсэн дээд талын мөрөнд 1 гэсэн утгаа бичээд Value Label гэсэн доод талын мөрөнд тухайн тоо ямар утга агуулгатай мэдээллийг илэрхийлж байгааг зааж өгнө. Add товчийг дарснаар тухайн утга маань тодорхойлогдож дуусна. Дараа нь Value талбарт 2 гэсэн тоог бичиж, Value Label талбарт 2-ын тоогоор ямар утгыг илэрхийлж байгааг бичээд Add товчийг дарна. Гэх мэтчилэн тухайн хувьсагч хичнээн утгатай байна, тэдгээр утгуудаа нэг нэгээр нь дэс дараалан тодорхойлж дуусгана. Зураг дээр 1 гэсэн тоогоор эрэгтэйг тэмдэглэснийг 1=”Male” хэмээн тодорхойлсон байна. Харин 2 гэсэн тоогоор эмэгтэйг тэмдэглэснийг хараахан тодорхойлж дуусаагүй, Add гэсэн товчийг дарснаар доод талын дэлгэцэнд шилжиж орсноор бүрэн тодорхойлогдож дуусах юм. Yүний дараа ОК товчийг дарснаар шинж тэмдгийн утгуудыг тодорхойлох үйлдэл бүрэн дуусах ажээ. Хэрвээ тоо болон утгаа буруу бичээд Add товч дарчихсан бол санаа зовох зүйлгүй. Тухайн буруу тодорхойлогдсон утгаа сонгоод Remove товчийг дарснаар устгаж, дахин шинээр тодорхойлох боломжтой. Эсвэл тухайн утгаа сонгоод засварлаж Change товчны тусламжаар шинэчилж сольж болдог.

Missing гэсэн талбарт тооцооноос хасах утгуудаа зааж өгдөг. Практикт аливаа мэдээллийг 100% бүрэн дүүрэн цуглуулах нь ховор. Респондентууд хариулахаас татгалзсан, эсвэл бусад шалтгаанаар бөглөгдөөгүй асуултууд цөөнгүй тохиолдоно. Эдгээр хариулт байхгүй нүднүүдийг Missing value хэмээн үздэг. Боловсруулалтанд Missing value буюу хариулагдаагүй утгуудыг оролцуулах буюу оролцуулахгүйгээр хоёр янзаар тооцоо хийж болдог. Түүнээс гадна зарим тохиолдолд хариулт байгаа боловч тэр нь үнэнээс гажиж байгаа, хэт хазайлттай гэж үзсэн утгуудыг, зарим тохиолдолд идэвхгүй дундыг барьсан (мэдэхгүй, хариулаагүй, хариулахыг хүсэхгүй байна, тодорхой санал алга, хэлэхэд хэцүү г.м) хариулт бүхий утгуудыг Missing value хэмээн үзэж тооцооноос хасаж болдог. Yүнийг Missing талбарт зааж өгнө.

Зурагт үзүүлсэн жишээн дээр 0-59 гэсэн [0,59] завсрын бүх утгууд, дээр нь 100 гэсэн дискрет утгыг Missing value хэмээн заахаар сонгож авчээ.

Measure буюу хэмжээс. Энэ талбарт шинж тэмдгийн хэмжилтийн түвшинг тодорхойлно. Метрийн шкалаар хэмжигдсэн утгыг Scale, эрэмбийн шкалаар хэмжигдсэн шинж тэмдгийг Ordinal, нэрлэсэн шкалаар хэмжигдсэн шинж тэмдгийг Nominal хэмээн тодорхойлно.

Хувьсагчийг тодорхойлох үйлдлийг хийж дуусгаснаар мэдээллийг боловсруулахад бэлтгэх шат үндсэндээ дуусаж байгаа юм. Энэ хүртэл бидний хийж гүйцэтгэсэн үйлдлүүд хэдий чухал ч гэсэн бидний үндсэн зорилго биш юм. Дараа дараагийн удаад мэдээллийг боловсруулах талаар цуврал зөвлөгөөг хүргэх болно.

Modul SPSS 1 ini akan mebahas tentang pengantar statistik dan SPSS. Materi yang dibahas meliputi:

• Konsep/Pengertian Statistik,
• Sejarah Statistik,
• Aplikasi Ilmu Statistik
• Jenis Data Statistik
• Sampling dan Populasi
• Membuka SPSS
• Cara kerja SPSS

Sekilas Tentang Statistik

Pada mulanya, kata statistik diartikan sebagai keterangan-keterangan yang dibutuhkan oleh negara dan berguna bagi negara. Keterangan-keterangan tersebut umumnya dipergunakan untuk memperlancar penarikan pajak dan mobilisasi rakyat jelata ke dalam angkatan perang. Tiap akhir bulan Desember, Caesar Agustus dari zaman Romawi mengeluarkan sebuah dekrit agar setiap orang kembali ke kota masing-masing dan melakukan registrasi. Registrasi tersebut meliputi keterangan-keterangan mengenai nama, usia, jenis kelamin, pekerjaan dan jumlah keluarga penduduk negara. Sebenarnya, keterangan-keterangan kuantitatif semacam itu kini lebih dikenal dengan nama sensus. Lambat-laun, statistik diartikan sebagai data kuantitatif baik yang masih belum tersusun maupun yang telah tersusun dalam bentuk tabel. Statistik diartikan sebagai kumpulan data yang berwujud angka-angka. Hingga kini, pengertian tersebut masih populer dan tetap melekat dalam pikiran masyarakat.

Penggunaan metode statistik dalam penelitian ilmiah sebetulnya telah dirintis sejak tahun 1880 ketika F.Galton pertama kali menggunakan korelasi dalam penelitian ilmu hayat. Pada waktu itu, penggunaan metode statistik dalam penelitian biologi maupun sosial tidak dapat dikatakan lazim. Bahkan pada akhir abad ke sembilanbelas, kecaman-kecaman pedas acapkali dilontarkan terhadap Karl Pearson yang memelopori penggunaan metode statistik dalam berbagai penelitian biologi maupun pemecahan persoalan yang bersifat sosio-ekonomis.

Kini, setelah lebih dari seabad lamanya, tiada seorang sarjana peneliti yang menyangkal betapa pentingnya metode statistik bagi penelitian ilmiah. Tanpa metode statistik, peneliti seakan-akan seorang buta meraba-raba dalam kegelapan untuk menangkap sesuatu yang belum tentu ada. Meskipun demikian, metode statistik modern seperti yang dikenal dan yang dipergunakan peneliti ilmiah di bidang biologi, pertanian dan ekonomi merupakan produk abad ke duapuluh dan memperoleh kemajuan yang pesat sejak tahun 1918 – 1935 ketika R. Fisher memperkenalkan analisa varians ke dalam literatur statistik. Sejak itu, penggunaan metode statistik makin meluas dari bidang biologi dan pertanian ke bidang-bidang pengetahuan lainnya. Bidang-bidang ilmu pengetahuan biometri, agronometri, ekonometri, psikometri, sosiometri dan anthropometri telah memperoleh kemajuan karena perkembangan yang pesat dari metode statistik modern.

Dowload pdf:

Modul SPSS 1 – Pengatar Statisitik dan SPSS

SPSS програмыг хэрэглэх нь – 3

Үүний өмнө бид та бүхэнд аливаа судалгааны Database буюу өгөгдлийн сан ямархуу бүтэцтэй байдаг болон судалгааны анхдагч мэдээллийг компьютерт хэрхэн оруулах талаар зарим зөвлөгөөг хүргэсэн билээ. Энэ удаад Excell програм ашиглан оруулсан мэдээллийг (Excell -ийн файлыг) SPSS програм руу хэрхэн татаж авах болон хувьсагчдыг хэрхэн тодорхойлох талаар зөвлөгөө өгөх болно.

            Хэрэв та санаж байгаа бол Excell програм ашиглан оруулсан мэдээллийн маань багана бүр тус тусдаа бие даасан хувьсагч болох бөгөөд бүгд өөр хоорондоо ялгаатайгаар нэрлэгдсэн байх ёстой. (Энэ нь баганын хаягнаас ялгаатай) Бидний толгой мөр болгон оруулсан хувьсагчдын нэрс маань тухайн өгөгдлийг SPSS програм руу татаж авах үед автоматаар мөнхүү хувьсагчийн нэр болж ордог. SPSS програмд яг адил нэртэй хоёр өөр хувьсагч байж болохгүй учраас Excell програмын толгой мөрөнд оруулсан хувьсагчдын нэр (Энэ нь Excell програмд хэдий бололцоотой ч гэсэн) өөр хоорондоо ялгаатай байх ёстой юм. Та толгой мөрийг зөв оруулж, хувьсагчдаа зөв нэрлэчихсэн, бүх мэдээллээ компьютерт шивж оруулаад дахин дахин сайтар шалгачихсан бол одоо мэдээлэл оруулах үе шат дууслаа. Одоо Excell програмаасаа гарч мэдээллээ SPSS програм руу татаж авахад бэлэн болсон байна.

 Бид дараагийн шатанд мэдээллийг SPSS програмын Data Editor цонх руу татаж авч, хувьсагчийг тодорхойлон, мэдээллийг боловсруулахад бэлтгэх ажлыг гүйцэтгэнэ.

            SPSS програмыг ажиллуулъя. Ингэхэд  хамгийн түрүүнд Data Editor цонх идэвхжих ба юуны өмнө 6 үндсэн сонголт бүхий харилцах цонх нээгдэнэ.  

Хэрэв та SPSS програмыг анхлан ашиглах гэж байгаа бол энэ програмын талаарх товч ойлголт авахын тулд хамгийн эхний Run the tutorial гэсэн сонголтыг идэвхжүүлж OK товчийг дар.  Энэ нь SPSS програмыг эхлэн суралцагчдад зориулсан хичээл юм.

• Type in data сонголтыг хийснээр мэдээлэл оруулах засварлах үйлдлийг гүйцэтгэнэ.
• Run an existing query сонголтоор өмнө нь үүсгэсэн Query файлыг ажиллуулна.
• Create new query using Database Wizard сонголтоор өгөгдлийн сан ашиглан шинэ Query үүсгэх үйлдлийг гүйцэтгэнэ.
• Open an existing data sourse -ийг сонгосноор өмнө нь үүсгэсэн өгөгдлийн файлуудаас хэрэгтэй өгөгдлөө сонгож нээх боломжтой.
• Open another type of file сонголтоор SPSS-ийн өөрийнх нь sav гэсэн өргөтгөлтэй файлуудаас өөр, бусад төрлийн  өгөгдлийн файлуудыг нээнэ.

Бид судалгааны мэдээллийг EXCELL програм ашиглан оруулах талаар зөвлөсөн учраас одоо түүнийгээ SPSS рүүгээ хэрхэн татаж авах талаар зөвлөе. Үүний тулд 5 дахь сонголт буюу өгөгдлийн файл нээх сонголтыг идэвхжүүлнэ. Ингэхдээ SPSS програмын өгөгдлийн файл биш, өөр төрлийн файл нээх гэж байгаа учир More files… гэдгийг сонгон OK товч дарна. Ингэхэд жирийн Open file буюу файл нээх цонх гарч ирэх бөгөөд энэ цонхны Files of type буюу файлын төрөл гэсэн талбараас Excel (*.xls) гэснийг сонгож идэвхжүүлснээр xls гэсэн өргөтгөлтэй файлууд цонхонд ил гарч харагдана. Одоо нээх гэж байгаа файлаа сонгож аваад Open товчийг дарвал дараах цонх гарч ирдэг.

Энэ цонхонд бид тухайн файлын аль Sheet -ээс, ямар мужид байгаа мэдээллүүдийг сонгон авч Data Editor цонхонд оруулах вэ гэдгээ зааж өгөх юм. Yүний дараа ОК товчийг дарвал бидний сонгож авсан мэдээлэл  Data Editor цонхонд орж ирэхийн зэрэгцээ, эхний мөрийг автоматаар хувьсагчуудын нэр болгож авдаг. Үүний тулд зураг дээр байгаатай адилаар Read variable names from the first row of data. товч идэвхжүүлэгдсэн байх ёстой. Хэрэв энэ товчийг идэвхжүүлээгүй бол Variable name буюу хувьсагчдийн нэрийг толгой мөрнөөс уншилгүйгээр бүх хувьсагчдийг ерөнхий нэр өгч эхнээс нь дэс дараалан (VAR0001, VAR0002 . . . г.м) дугаарладаг.

            Таны өгөгдөл бүгд үнэн зөв алдаагүй шивэгдсэн бол та өгөгдлөө Data editor цонхонд татаж авснаар мэдээллээ боловсруулах ажилдаа орж болно. Хэрэв зарим нэг нүдэнд алдаа гарсан, өөр төрлийн өгөгдөл оруулсан зэргээс үүдэн SPSS програм руу хөрвүүлэх боломжгүй болвол Output цонх идэвхжиж, татаж авч чадаагүй мэдээлэл бүхий нүднүүдийн талаарх сэрэмжлүүлэг гарч ирнэ. Та үүнийг ашигласнаар аль баганын, хэд дэх мөрний ямар мэдээлэл бүрэн  танигдаж чадахгүй байгааг олж илрүүлэн засах боломжтой. Амжилт хүсье.

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I just got back from the IBM Information on Demand (IOD) conference in Las Vegas.  A key message was that the future is in analytics and predictive analytics at that.  IBM has already invested $12B ($8B acquisitions, $4B organic growth) in analytics since 2005.  Its recent purchase of SPSS has enabled the company to put a stake in the ground regarding leading the analytics charge.

Predictive analytics uses historical data to try to predict what might happen in the future.  There are different technologies that can help you to do this including data mining and statistical modeling.  For example, a wireless telecommunications company might try to predict churn by analyzing the historical data associated with customers who disconnected the service vs. those that did not.  Attributes that might serve as predictors include dropped calls, calling volume (in network, out of network), demographic information, and so on.  An insurance company might try to predict future fraud using past claims that where the outcome is known.  Adam Gartenberg’s blog describes more examples of this.  IBM plans to make predictive analytics more pervasive in several ways. 

• Making models easier to build. It will make predictive modeling tools easier to use for those who build the models.  A good example of this is the SPSS PASW Modeler product that uses a visual paradigm to build various kinds of models.  I stopped by the SPSS booth at the show and saw the software at the demo area and it is nice with lots of feature/functionality built into it.  Training is available (and I would argue necessary), for example, to understand when you might want to use a certain kind of model. 
• Embedding the predictive model in a process.  Here, the predictive model would become part of a business process. For example, a predictive model might be built into a claims analysis process.  The model determines the characteristics and predictors of claims that might be classified as fraudulent.  As the claims come through the process, those that are suspicious, based on the model, would get kicked out for further examination.  

So, given these two approaches, can predictive analytics become pervasive? 

In the case of making predictive modeling tools easier to use, the question isn’t whether someone can use a tool, but whether he or she can use it correctly.   The goal of a tool like PASW is to enable business users to build advanced models. Could a BI power user who is accustomed to slicing and dicing and shaking and baking data effectively use a tool like this?  Possibly, if they have the right thought process and they pay attention to the part of the training that describes what type of technique to use for what type of problem.  It is a good goal.  Time will be the judge.

As for embedding predictive analytics in business processes; this is already starting to happen and here is where the possibility of making prediction more pervasive gets exciting.  For example, telecommunications companies can embed predictive analytics into a call center application to understand an action that a customer might take.  A call center representative can make use of the results of the model (without understanding the model or what it does).  He or she is simply fed information, from the model, (in real time) to help service a customer most effectively.   The model can be created by a skilled analytics person, but deployed in such a way that it can help a lot of other people across an organization.  One key will be the ability to integrate a model into the actual code and culture behind a business process.

Look, I don’t have a crystal ball (little predictive modeling humor there), but I am very excited about the possibilities of predictive modeling.  I did this kind of modeling for years at Bell Laboratories, way back when, and it is great to see it finally gaining traction in the marketplace.  Predictive analytics can be a truly powerful weapon in the right hands.

Went to SPSS Directions Japan 2009 last week. was quite impressed by the fact that the SPSS moder (used to be SPSS Clementine) actually has the functionalities to load and transform data. though haven’t done any data mining exercise after coming out of school, but still remember how difficult (time consuming) it was to transform and aggregate the transaction data into a meaningful way (such as customer level) to do some analysis. there were also several interesting session, from which I had some thoughts as below mainly for pharm sales management.

• Stop endless targeting and start some CRM, when talking about CRM the fundamental process is quite simple. Acquisition, get the potential customer seeds, target (discriminant analysis) those with the high purchase possibility within available budget and then take action on them. Grow Value , segment the acquired customer (clustering analysis, decision tree etc) and tailor your offer to maxims the customer value. Long term Retention, identify signals of losing customer and take proper action based on the cost/value. these very basic strategies and analysis have been used in consumer market for years. Well if you look at how pharm companies let their MRs target doctors year after year, sometimes every half year, which to me is really a waste a MRs time (sometime demotivating) to flag same doctors again and again in the system, if the targeting is really about acquisition, about new customer. We really have to stop this endless targeting cycle and think about how much do we know our existing customer, to segment them, and try to grow the relationship by up-sell and cross-sell, also to identify risk (behavior or attitude changes) which may lead to lose them to our competitors.

• applying Marketing Research in sales operation, it is also quite common process to first conduct concept test then test in field, finally to mass market when you develop a new product in consumer market. And in the each step of these marketing research process, there are data collection and data analysis with some statistical procedure. What if we do the similar practice when implementing sales strategy, or when implementing a new IT tool to MRs. a quick example could be survey what features or information MR wants (conjoint analysis), then test product (the new sales strategy or tool) to validate before mass roll-out.

• territory design with sales achievement histogram, didn’t have a clear thoughts yet but somehow felt there must be a way from historical data to validate if the territory assigned to a MR is fair enough (p<0.05).

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After just returning from IBM’s Information on Demand (IOD) Conference in Las Vegas, I would like to take this opportunity to virtually whisper just one word in the ear of a current day Benjamin Braddock, “analytics”. Many businesses have spent the past 25 years or so automating and streamlining business processes in order to drive improvements in efficiency and productivity.  But now, it is becoming apparent that these businesses expect their future success will increasingly depend on how skillfully they manage, govern, and analyze information. Businesses are applying analytical techniques to business information to help reduce risk and increase the certainty that they are making the right decisions.

IBM has, in fact, spent $12 Billion in software investments (both organic multiple acquisitions like SPSS, Cognos, Filenet, iPhrase, and Ascential Software, just to name a few) over the past 4-5 years to ensure it will be able to support its customers in their quest to unlock the business value of information. In addition, in April of 2009 IBM announced a new organization comprised of 4000 consultants focused on advanced business analytics and business optimization – teams with skills in applying business intelligence technologies like mathematical modeling, simulation, data analytics, and optimization techniques.

In an era of intense competition, tight credit, and cost concerns across global and vertical markets, this focus on getting the most value from the information you have makes a lot of sense. Companies find they are processing more information than ever before, but less of this information is being accurately and adequately used.  The quantity of available data that a business needs to manage and understand has skyrocketed along with the increase in instrumented and intelligent products. For example, RFID tags that are embedded in manufactured products,  plants and animals generate an enormous amount of data in efforts to control inventories and improve security and safety.  Trying to make decisions with inadequate,  inaccurate, or untimely  information is like driving a fast sports car down the highway with a very large blind spot impeding your view of the truck approaching on your side. You need to know about the obstacles that might appear in  your pathway before you try to make a “real-time” correction and steer your car (or your business) of a cliff.  So, students and business leaders alike please take note, I see some “analytics” in your future.