| Publisher | SAS Institute | ||
|---|---|---|---|
| Format | 140.7KB PDF | Date added | 14 May 2007 |
| Topics | Knowledge and Data Management, Data Mining - Analysis | ||
| Downloads | 24 | ||
Many statisticians, business analysts and other quantitative analysts are required to present the results of their modeling efforts to non-quantitatively trained decision-makers, or to decision-makers whose level of statistical training may be very limited. In the predictive modeling realm, where very sophisticated statistical tools such as logistic regression, neural networks and Chi-Square Automatic Interaction Detection (CHAID) are commonly employed, the knowledge gap between the provider and consumer of the analysis can become quite wide. When this occurs, the fruits of the analysis may go un- or underutilized. This paper discusses several ways that the results of predictive models can be effectively communicated to non-technical decision-makers.
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