The importance of developing soft skills competency among students should be the priority of all the Higher Educational Institutions (HEIs) in order to ensure their graduates are marketable. Therefore, it is essential for HEIs to distinguish the knowledge and soft skill levels of their students so that strategies and intervention could be implemented to rectify their capabilities. The main purpose of this study is to evaluate the knowledge and soft skills competency from the employer's viewpoints on the Universiti Utara Malaysia (UUM) students participating in the industrial training programme. A total of 438 employers from different industrial backgrounds had participated in this study. A questionnaire consisting of five dimensions of soft skills which are basic knowledge, communication skills, practical skills, leadership, and attitude was utilized to collect data. The results of this study indicate that the employers were satisfied with the knowledge and soft skills competency portrayed by UUM students in preparing themselves for the real work environment. The employers from the service sectors were satisfied with students' performance in all dimensions of soft skills measured. However, employers from the factory and commerce sector perceived as moderate satisfaction for all dimensions of soft skills. Additionally, the employers of the factory and commerce sector assessed by giving the lowest satisfaction score for "hands-on" skills, but generally they satisfied with the students' communication skills. The information gathered can provide important insights from the perspective of organizations which is valuable in improving the overall hard and soft skills competency for future professionals and managers.

Non-parametric smoothing of the location model is a potential basis for discriminating between groups of objects using mixtures of continuous and categorical variables simultaneously. However, it may lead to unreliable estimates of parameters when too many variables are involved. This paper proposes a method for performing variable selection on the basis of distance between groups as measured by smoothed Kullback-Leibler divergence. Searching strategies using forward, backward and stepwise selections are outlined, and corresponding stopping rules derived from asymptotic distributional results are proposed. Results from a Monte Carlo study demonstrate the feasibility of the method. Examples on real data show that the method is generally competitive with, and sometimes is better than, other existing classification methods.

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Distance criteria are widely applied in cluster analysis and classification techniques. One of the well known and most commonly used distance criteria is the Mahalanobis distance, introduced by P. C. Mahalanobis in 1936. The functions of this distance have been extended to different problems such as detection of multivariate outliers, multivariate statistical testing, and class prediction problems. In the class prediction problems, researcher is usually burdened with problems of excessive features where useful and useless features are all drawn for classification task. Therefore, this paper tries to highlight the procedure of exploiting this criterion in selecting the best features for further classification process. Classification performance for the feature subsets of the ordered features based on the Mahalanobis distance criterion is included.

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