Overview of Descriptive Statistics - Teradata Warehouse Miner

In-Database Analytic Functions User Guide

Product
Teradata Warehouse Miner
Release Number
5.4.5
Published
February 2018
Language
English (United States)
Last Update
2018-05-04
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dfw1503087325991.ditamap
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B035-2306
lifecycle
previous
Product Category
Teradata® Warehouse Miner
Descriptive statistics provides a variety of functions to statistically analyze and explore a Teradata database. Descriptive statistical analysis is valuable for several reasons:
  • It provides business insight.
  • It uncovers data quality issues, which, if not corrected or compensated for, would jeopardize the accuracy of analytic models based on the data.
  • It isolates the data used in building analytic models. For example, outlying values should sometimes be excluded from a model; in other cases, these values might be required to solve a particular business problem.
  • Some statistical processes used in analytic modeling require a certain type of data distribution. Descriptive statistical analysis can help determine the suitability of various data elements for model input and can suggest transformations that may be required.