The purpose of predictive modelling is to predict the variable of interest with reasonable precision, and often to assess the contribution of the independent variables to the dependent variable. In this paper, all of the works examined are aimed at predicting concentrations of outdoor PM10 concentrations. The vast majority of the works reported used almost exclusively predictors of the meteorological and source emissions. However, the use of the Hybrid model in predicting PM10 concentrations is still not widely used in Malaysia.
In mathematics, the students are urged to answer the questions correctly. Answers with complete sets of solutions shows a certain level of understanding of students. However, it is undeniable that some student had difficulty in answering the questions correctly. The students may not have certain understanding on a particular topic and that does not mean that they are poor in mathematics. Some errors that students do in doing mathematics may due to misunderstanding of questions, incorrect concepts, careless mistakes or skip of required answer steps. The purpose of this study was to give insight to the instructors on the common errors done by the students in solving question with long sets of solution. This research method is a descriptive study, with the aim of finding out the number of percentage and the level of students' mistakes using Newman's Error Analysis. This study focused on year two student that undertook Further Calculus in Engineering emphasised on convergent test of power series using ratio test topic. The data were collected from their final examination answer papers, focused only on related questions. The results show the most common error made by the students were transformation error (38%) and encoding error (38%) and did less in comprehension error (2%). While reading error (5%) and process skill error (17%) could also had been considered low. Instructors must guide the students more on correct transformation (solve fraction and factorisation) and encoding (interval of convergence) in order to solve convergence of power series using ratio test.
This study develops an Athlete Performance Capabilities Index (APCI) model using multivariate analysis for selecting the best player of under twelve (U12). Measurement of anthropometrics and physical fitness were evaluated among 178 male players aged 12±0.52 years. Factor score derived by Principal Component Analysis were used to obtain a model for APCI and Discriminant Analysis (DA) were conducted to validate the correctness of group classification by APCI. Result was found two factors with eigenvalues greater than 1 were extracted which accounted for 62.00% of the variations present in the original variables. The two factors were used to obtain the factor score coefficients explained by 35.72% and 26.67% of the variations in athlete performance respectively. Factor 1 revealed high factor loading on fitness compared to Factor 2 as it was significantly related to anthropometrics. A model was obtained using standardized coefficient of factor 1. Three clusters of performance were shaped in view by categorizing APCI ≥ 75%, 25% ≤ APCI < 75% and APCI < 25% as high, moderate and low performance group respectively. Three discriminated variables out of thirteen variables were obtained using Forward and Backward stepwise mode of DA, which were weight, standing broad jump, and 40 meters’ speed. Such variables were established as essential indicator for selecting the best player among male U12.
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