The aim of this thesis was to investigate the use of effect sizes to report the results of
statistical credit rating models in a more practical way. Rating systems in the form of
statistical probability models like logistic regression models are used to forecast the
behaviour of clients and guide business in rating clients as “high” or “low” risk borrowers.
Therefore, model results were reported in terms of statistical significance as well as business
language (practical significance), which business experts can understand and interpret. In this
thesis, statistical results were expressed as effect sizes like Cohen‟s d that puts the results into
standardised and measurable units, which can be reported practically. These effect sizes
indicated strength of correlations between variables, contribution of variables to the odds of
defaulting, the overall goodness-of-fit of the models and the models‟ discriminating ability
between high and low risk customers. / Statistics / M. Sc. (Statistics)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/18790 |
| Date | 12 1900 |
| Creators | Steyn, Hendrik Stefanus |
| Contributors | Ndlovu, P. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (xi, 85 leaves) : illustrations, application/pdf |
Page generated in 0.002 seconds