“Can I use what I learnt at work?” Accounting education-practice gap

Approaching graduation, the first question for every student is “Will I be able to find a job?” If they find a job, the next question will properly be “Can I perform well at work” This research investigated whether students can use what they have learned when they start work, whether there is a gap, and what gap exists between accounting education and the requirements of practice. This study aims to identify the gap of expectation, skill obtained and skills required between student and practitioners’ point of view. This will provide information to students on what areas should they be paying attention to during study, and inform educators on where gaps exist, and brief employers on areas a graduate of accounting education may lack, so focused training can be provided. A questionnaire was designed using free survey tool Qualtrics. Questions were adapted from four different sources. The survey was sent to a Wintec CBITE accounting tutor. With their consent and permission, the survey link was posted onto a Moodle page to share with Wintec CBITE students. The researcher then collected data though Qualtrics. Analysis was done though Excel. A table was created for each question. Some answers were modified to match the literature’s format of presenting their result to show a fairer and equal comparison. The researcher received a total of 26 responses.,20 of which were from accounting major students. However, only 15 out of the 20 had completed the whole questionnaire. Comparison of the results gained from this study contrast with the literature. Conclusions and recommendations are still to be made

A sparse conditional Gaussian graphical model for analysis of genetical genomics data

Genetical genomics experiments have now been routinely conducted to measure both the genetic markers and gene expression data on the same subjects. The gene expression levels are often treated as quantitative traits and are subject to standard genetic analysis in order to identify the gene expression quantitative loci (eQTL). However, the genetic architecture for many gene expressions may be complex, and poorly estimated genetic architecture may compromise the inferences of the dependency structures of the genes at the transcriptional level. In this paper we introduce a sparse conditional Gaussian graphical model for studying the conditional independent relationships among a set of gene expressions adjusting for possible genetic effects where the gene expressions are modeled with seemingly unrelated regressions. We present an efficient coordinate descent algorithm to obtain the penalized estimation of both the regression coefficients and the sparse concentration matrix. The corresponding graph can be used to determine the conditional independence among a group of genes while adjusting for shared genetic effects. Simulation experiments and asymptotic convergence rates and sparsistency are used to justify our proposed methods. By sparsistency, we mean the property that all parameters that are zero are actually estimated as zero with probability tending to one. We apply our methods to the analysis of a yeast eQTL data set and demonstrate that the conditional Gaussian graphical model leads to a more interpretable gene network than a standard Gaussian graphical model based on gene expression data alone.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS494 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org