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IMSTAT (Analytics) Procedure
Syntax
Procedure Syntax
PROC IMSTAT (Analytics) Statement
AGGREGATE Statement
ARM Statement
ASSESS Statement
BOXPLOT Statement
CLUSTER Statement
CORR Statement
CROSSTAB Statement
DECISIONTREE Statement
DISTINCT Statement
FORECAST Statement
FREQUENCY Statement
GENMODEL Statement
GLM Statement
GROUPBY Statement
HISTOGRAM Statement
HYPERGROUP Statement
KDE Statement
LOGISTIC Statement
MDSUMMARY Statement
NEURAL Statement
OPTIMIZE Statement
PERCENTILE Statement
RANDOMWOODS Statement
REGCORR Statement
SUMMARY Statement
TEXTPARSE Statement
TOPK Statement
TRANSFORM Statement
QUIT Statement
Overview
Using
Temporary Tables, Generated Code, and Scoring
Generated Code and Locale Sensitivity
Examples
Calculating Percentiles and Quartiles
Retrieving Box Values
Retrieving Box Plot Values with the NOUTLIERLIMIT= Option
Retrieving Distinct Value Counts and Grouping
Performing a Cluster Analysis
Performing a Pairwise Correlation
Crosstabulation with Measures of Association and Chi-Square Tests
Training and Validating a Decision Tree
Storing and Scoring a Decision Tree
Performing a Multi-Dimensional Summary
Fitting a Regression Model
Forecasting and Automatic Modeling
Forecasting with Goal Seeking
Aggregating Time Series Data
Training and Validating a Neural Network
Predicting Email Spam and Assessing the Model
Transforming Variables with Imputation and Binning
Examples
Example 1: Calculating Percentiles and Quartiles
Example 2: Retrieving Box Values
Example 3: Retrieving Box Plot Values with the NOUTLIERLIMIT= Option
Example 4: Retrieving Distinct Value Counts and Grouping
Example 5: Performing a Cluster Analysis
Example 6: Performing a Pairwise Correlation
Example 7: Crosstabulation with Measures of Association and Chi-Square Tests
Example 8: Training and Validating a Decision Tree
Example 9: Storing and Scoring a Decision Tree
Example 10: Performing a Multi-Dimensional Summary
Example 11: Fitting a Regression Model
Example 12: Forecasting and Automatic Modeling
Example 13: Forecasting with Goal Seeking
Example 14: Aggregating Time Series Data
Example 15: Training and Validating a Neural Network
Example 16: Predicting Email Spam and Assessing the Model
Example 17: Transforming Variables with Imputation and Binning
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