Data Quality for Analytics Using SAS

Chapter 13: Data Quality with Analytics

13.1 Introduction

13.2 Benefit of Analytics in General

Outlier detection

Missing value imputation

Data standardization and de-duplication

Handling data quantity

Analytic transformation of input variables

Variable selection

Assessment of model quality and what-if analyses

13.3 Classical Outlier Detection

Ways to define validation limits

Purpose of outlier detection

Statistical methods

Implementation

Outlier detection with analytic methods

13.4 Outlier Detection with Predictive Modeling

General

Methods in SAS

Example of clinical trial data

Extension of this method

13.5 Outlier Detection in Time Series Analysis

General

Time series models

Outlier detection with ARIMA(X) models

Decomposition and smoothing ...