1. THE
BEGINNING

2. Learning Objecitve Be prepared to use the auto numeric node in IBM
SPSS Modeler. Having used the node, identify the
best model. Be prepared to interpret the outputs
of CHAID, Regression, Linear, KNN (K Nearest
Neighbors) Algorithm, and C&R (Classification and
Regression) Tree.

3. $ Productivity Drivers Creation of Value Through Exchange
Competition: $ $ (Falling Prices) $
Productivity
Drivers
$ $ $
$ $ $
$ $ $
$ $ $
Mass Marketing Customize, target low incidence, high value customers
Mass Customization
DATABASE
Marketer Pushes Product (Queries, Mail Merge) Customer Pulls Product (Web App)
1 to to 1
Segmentation
& Targeting
Identifying Segments
Measuring Market Segment Value:
Predicting Consumer Response (Models)
Cluster Analysis
Gain Scores
Lifetime Value Analysis
Non-Statistical
Statistical
Table Design
Relationship Editor
--Joins: 1 to 1, 1 to ∞. ∞ to ∞
Queries
--Select Query (Bring data from one or more tables into virtual table.)
● Sort
● And / Or Logic
--Inner / Outer Join
RFM (current customers)
Market Basket Analysis (Web, Directed Web, Apriori)
Data Attributes
Data Types: Norminal, Ordinal, Interval, Ratio
Data Attributes: Central Tendency, Spread
Relationship Tests
Correlation: Pearson (Interval/Ratio), Spearman (Ordinal)
Difference Tests
T – Test (Nominal/Interval or Ratio)
Mann-Whitney U (Nominal/Ordinal)
Chi Square (Nominal/Nominal or just Nominal)
Comprehensive Models
CHAID
Regression (simple & multiple)
IBM Modeler Autonumeric (compares multiple models)

12. KNN Algorithm
K Nearest Neighbors
This procedure plots all observations
in multidimensional space, here on
three dimensions for volatile acidity,
alcohol, and Award. Each wine is
plotted in this space. The place of
each wine can be determined by
putting the cursor on a dot. When
that happens, the number of the
observation is given and its
dependent variable value. In this
case, wine 1002 has a quality value
of 5. Values are represented by the
darkness of the dot, as indicated by
the key on the left of the chart.

13. CHAID Output in Viewer

14. C&R Tree uses a GINI purity measure (as CHAID
uses chi square) to split off branches that
differentiate groups with different patterns of
response. It always splits
into two branches.
Improvement is a
measure of increase
in the purity measure
following the spilt.

15. The Regression node functions the
same way as it would in SPSS. It uses
the Enter method, i.e., uses the
predictors in the same order that you
list them. The outputs are interpreted
in exactly the same way as they would
be in SPSS.

16. Regression Prediction Calculator

17. The Linear node is a regression node that works with
transformed data. In other words, the data is modified
in certain ways (e.g., trimming outliers) before running
the regression. While the printouts are somewhat
different from those for the regression node, they should
be interpreted in the same
way. The r – square value is
given in the Accuracy pane,
i.e., 36.5% of the variance is explained
by this model. The coefficients and
p – values are shown when you put
the cursor over one of the line. For
nominal variables, the coefficient is
given for all forms except the baseline
value. Here Award = 1 is the baseline.
No coefficient is given for that value.
Use coefficient given X 1 for
Award = 0.

18. Nominal & Ordinal Variables in Linear Node
If you have a nominal or ordinal variable in the Linear node,
a coefficient will be reported for all values of the variable except
one, the baseline value. Suppose you have a nominal ethnicity
variable with the following values: 1 Hispanic, 2 Black, 3 Asian,
4 Caucasian, 5 Native American. The Linear node would report
4 coefficient values, e.g., for values 1, 2, 3, and 4. At least one value
will not be given, e.g., 5. The missing value or values are the baseline.
In calculating the predicted value if ethnicity is Asian, the coefficient for
3 Asian would be used (with an X value of 1) to get the
predicted value. If the ethnicity is Native American, just calculate
the predicted value from all coefficients except ethnicity to get
this baseline group predicted value.

19. We have a coefficient for all of the
ethnic groups except group 5, Native
Americans. To get the predicted Native
American value, just use Intercept, Alcohol,
and Volatileacidity.

20. THE
END