Browsing by Author "Steel, Sarel"

Now showing 1 - 3 of 3
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    A review and comparison of methods of testing for heteroskedasticity in the linear regression model
    (Taylor and Francis Ltd., 2025) Blignaut, Renette; Luus, Retha; Steel, Sarel
    This study reviews inferential methods for diagnosing heteroskedasticity in the linear regression model, classifying the methods into four types: deflator tests, auxiliary design tests, omnibus tests, and portmanteau tests. A Monte Carlo simulation experiment is used to compare the performance of deflator tests and the performance of auxiliary design and omnibus tests, using the metric of average excess power over size. Certain lesser-known tests (that are not included with some standard statistical software) are found to outperform better-known tests. For instance, the best-performing deflator test was the Evans-King test, and the best-performing auxiliary design and omnibus tests were Verbyla's test and the Cook-Weisberg test, and not standard methods such as White's test and the Breusch-Pagan-Koenker test.
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    Investigation of telecommunication recommender systems for curated post-paid products
    (University of the Western Cape, 2023) Eachells, Brent Jesse; Steel, Sarel
    Telecommunication recommender systems
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    Statistical modelling in enrolment management: a higher education case study
    (Elsevier, 2025) Brydon, Humphrey; Steel, Sarel; Mahlangu, Dineo
    Enrolment management is important to institutions of higher learning. Administrators at these institutions are annually faced by the question: how many offers for a given academic programme should be made to applicants to meet the registration target set by the authorities? Data on past and new applicants are available at most institutions. In this paper, data from the Faculty of Natural Sciences at the University of the Western Cape are used to develop a statistical model that provides estimates of the likelihood of new applicants accepting registration offers from the Faculty. The paper therefore contributes to the important field of strategic enrolment management. The paper shows how a statistical model estimated from historical data can assist administrators to determine the number of offers that should be extended to applicants to reach a given registration target.