In this study, we firstly investigate the attitudinal differences towards statistics between students (from a developing country) in service module and mainstream courses; and, secondly, differences in their attitudes over time, at the beginning and at the end of these courses. Knowledge regarding descriptive and inferential statistics are required for students at tertiary level in many disciplines, and the literature confirms (especially in developing countries) the under-preparedness (at all year levels), inadequate performance and low motivation of students in such courses. An international acknowledged instrument (SATS-36) revealed students' (from different faculties) initial attitudes towards statistics on six components (affect, cognitive competence, value, difficulty, interest and effort) and statistical significant differences between pre-and post-test data. The main implication from these findings are that students (in all faculties) tend to decrease in attitudinal scores over time, and educators can take awareness of this when designing pedagogy in statistics modules.

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Recently, Ross derived a theorem, namely the "Recovery theorem", that allows for the recovery of the pricing kernel and real-world asset distribution, under particular assumptions, from a forward-looking risk neutral distribution. However, recovering the real-world distribution involves solving two ill-posed problems. In this paper, the accuracy of a regularised multivariate mixture distribution to recover the real-world distribution is introduced and tested. In addition it is shown that this method improves the estimation accuracy of the real-world distribution. Furthermore, an empirical study, using weekly South African Top40 option trade data, is carried out to show that the recovered distribution is in line with economic theory.

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