Believe it Or Not

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Believe it Or Not

• Mike Speed

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Statistics Can Be Fun
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Learning Outcomes

• At the end of this talk, I hope that you will
• Realize that statisticians are real people with
  real lives
• Agree that Statistics is an important area
• Have an understanding of Data Mining

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Who Am I and Life Beyond Statistics

• Worked for NASA put the men on the moon
  1964-1969
• Teacher/Researcher
• Businessman
• Civic leader

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Outside of Statistics

• Wife, 2 children and 5 grandchildren
• Wife makes porcelain dolls
• Wife and I make pottery specialize in
  crystalline glazes
• We fish as often as we can

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Pictures
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Dianes Dolls
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Are You New To Statistics

• Yes
• No

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But
Nice you asked I am a brain surgeon
I am a statistician
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It is Nice to Be Alone
Just kidding around
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Shakespeare Statistics

• There is a lot in common
• Good Party Talk

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Shakespeare Statistics

• BEATRICE
• He set up his bills here in Messina and
  challengedCupid at the flight and my uncle's
  fool, readingthe challenge, subscribed for
  Cupid, and challengedhim at the bird-bolt. I
  pray you, how many hath hekilled and eaten in
  these wars? But how many hathhe killed? for
  indeed I promised to eat all of his killing.

And your literature professor would ask What
are the essential concepts that Shakespeare is
trying to convey? What is Shakespeare saying?
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Statistics
And we want to know what are the essentials
parts of the this data. What is the data saying
to us?
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Difference Between Shakespeare and Statistics

• Statistics has a set of rules and reasonable
  people will come to similar conclusions about the
  data
• In literature many different interpretations

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Where did the Shakespeare Quote Come From?

• Much Ado About Nothing!!

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Data Mining

• Data mining, or knowledge discovery, is the
  computer-assisted process of digging through and
  analyzing enormous sets of data and then
  extracting the meaning of the data.
• Data mining tools predict behaviors and future
  trends, allowing businesses to make proactive,
  knowledge-driven decisions.

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Example

• One Midwest grocery chain used data mining to
  analyze local buying patterns. They discovered
  that when men bought diapers on Thursdays and
  Saturdays, they also tended to buy beer. Further
  analysis showed that these shoppers typically did
  their weekly grocery shopping on Saturdays. On
  Thursdays, however, they only bought a few items.
  The retailer concluded that they purchased the
  beer to have it available for the upcoming
  weekend. The grocery chain could use this newly
  discovered information in various ways to
  increase revenue. For example, they could move
  the beer display closer to the diaper display.
  And, they could make sure beer and diapers were
  sold at full price on Thursdays.

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Another Example

• Merck-Medco Managed Care is a mail-order business
  which sells drugs to the country's largest health
  care providers Blue Cross and Blue Shield state
  organizations, large HMOs, U.S. corporations,
  state governments, etc. Merck-Medco is mining its
  one terabyte data warehouse to uncover hidden
  links between illnesses and known drug
  treatments, and spot trends that help pinpoint
  which drugs are the most effective for what types
  of patients. The results are more effective
  treatments that are also less costly.
  Merck-Medco's data mining project has helped
  customers save an average of 10-15 on
  prescription costs.

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Other Uses

• Market segmentation - Identify the common
  characteristics of customers who buy the same
  products from your company.
• Customer churn - Predict which customers are
  likely to leave your company and go to a
  competitor.
• Fraud detection - Identify which transactions are
  most likely to be fraudulent.
• Direct marketing - Identify which prospects
  should be included in a mailing list to obtain
  the highest response rate.

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More uses

• Interactive marketing - Predict what each
  individual accessing a Web site is most likely
  interested in seeing.
• Market basket analysis - Understand what products
  or services are commonly purchased together
  e.g., beer and diapers.
• Trend analysis - Reveal the difference between a
  typical customer this month and last.

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Another definition

• Data mining is the use of automated data analysis
  techniques to uncover previously undetected
  relationships among data items. Data mining
  involves the statistical analysis of data stored
  in a data warehouse. Three of the major data
  mining techniques are regression, classification
  and clustering.

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Misconceptionfrom tu
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Census 2000 Data Set

• The CENSUS2000 data is a postal code-level
  summary of the entire 2000 United States Census.
  It features seven variables
• ID postal code of the region
• LOCX region longitude
• LOCY region latitude
• MEANHHSZ average household size in the region
• MEDHHINC median household income in the region
• REGDENS region population density percentile
  (1lowest density, 100highest density)
• REGPOP number people in the region

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Census Data33,000 observations
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Data, Speak to Me with Thine Numbers

• What is going on here?
• What are the essential parts?
• Let us do a plot.

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Plot of Latitude and LongitudeColor by Density
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Pattern Discovery
The Essence of Data Mining? the discovery of
interesting, unexpected, or valuable structures
in large data sets. David Hand
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Pattern Discovery
The Essence of Data Mining? the discovery of
interesting, unexpected, or valuable structures
in large data sets. David Hand
If youve got terabytes of data, and youre
relying on data mining to find interesting things
in there for you, youve lost before youve even
begun.
Herb Edelstein
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k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Re-assign
cases. 6. Repeat steps 4 and 5 until
convergence.
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k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Re-assign
cases. 6. Repeat steps 4 and 5 until
convergence.
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k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Re-assign
cases. 6. Repeat steps 4 and 5 until
convergence.
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Demographic Segmentation Demonstration
Analysis goal
Group geographic regions into segments based on
income, household size, and population density.
Analysis plan
Select and transform segmentation inputs.
Select the number of segments to create.
Create segments with the Cluster tool.
Interpret the segments.
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SAS Program
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Skewed Data
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Transformed Data - log
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10 Clusters
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Select a Cluster
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What Can We Say?
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Where Do They Live
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Summary

• Statistics is a great field of study
• Thank you for being here and wanting to teach AP
  statistics
• We want to help you

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• Thanks