Students normally perceive statistics as a difficult subject, more so those who have weak mathematical foundation and low quantitative reasoning ability. This research attempts to determine the influence of prior mathematical knowledge and statistical reasoning on the outcome of the statistical performance of Diploma Science students in UiTM Campus Kuala Pilah, Negeri Sembilan. Paper-and-pencil assessment instruments that measure the prior mathematical knowledge, statistical knowledge and statistical reasoning ability were administered to 145 Diploma students. Data obtained were quantitatively analyzed using Structural Equation Modeling (SEM) approach. The results showed prior mathematical knowledge has a significantly moderate influence on their performance while statistical reasoning has negligible effect. Overall, the exogenous latent variables explained 37.2% of the total variation in the model. Furthermore, the study showed that there was a moderate relationship between prior mathematical knowledge and statistical reasoning but surprisingly no correlation between performance and statistical reasoning raising pertinent questions that are examined further in this paper.
Water is one of the most essential needs in human daily life. Water losses or Non-Revenue Water (NRW) refers to the treated water that has been produced from water plant which did not reach to the customer. This waste of water has caused the company to suffer losses and hence, burdens the people with increasing water tariff. Moreover, it becomes one of the challenges for commercial water system management because the water company must fulfil the demand from the society which keep increasing day by day. In addition, the demand for water is increasing, as the population is growing. Despite having the rainfall throughout the year in Malaysia, many cities are experiencing water shortage and frequent water supply disruptions. Therefore, efficient management of water distribution is required to minimise the water losses and to make sure the sustainability of water reserve for a long period. This study focuses on identifying the significant factors that influence the Non-Revenue Water and modelling the data using Multiple Linear Regression Model. The sample size used in this study were 212 observations and the variables involved were Length of Connection, Number of Connection, Production Quantity, Consumption Quantity and Non-Revenue Water. It is found that the variables of Number of Connection, Consumption Quantity and Production Quantity were significant to Non-Revenue Water whereas the variable of Length of Connection was not significant. It is hoped that the result from this study can be used by the water authority company in improving the water distribution and thus reduce water losses and cost.
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