The purpose of the present study was to explore the impact of perceived teachers' competence on students' performance moderated by perceived class environment. The sample consisted of 500 students (250 male & 250 female) taken from public and private schools from the rural areas of Dokota Town, Tibba Sultan Pur, and Mailsi. Participants' age range was between 12-14 years old. Teaching Competence Scale (Passi & Lalita, 2009) and Class Environment Scales (Kelly, 2010) were used to measure the perceived teachers' competence and perceived class environment, respectively. For the purpose of the present research, scales were translated into Urdu through back translation method. Results indicated that perceived teachers' competence predicted the students' performance, but did not predict the perceived class environment. Further results indicated that there is no mediation and moderation effect of class environment on the relationship between teachers' competence and students' performance.

Regression analysis has become popular among several fields of research and standard tools in analysing data. This structure was represented by four commonly statistical models such as multiple linear regression, fuzzy linear regression (Tanaka, 1982), fuzzy linear regression (Ni, 2005) and extended fuzzy linear regression by benchmarking models under fuzziness (Chung, 2012). Colorectal cancer (CRC) was applied on CRC cases in Malaysia. The CRC patients' quality of life in order to detect the CRC at an early stage is still very poor, the programmes are mainly ad-hoc and not implemented as a national wide programme. This study aims to determine the best model to measure the tumor size at hospitals using mean square error and root mean square error. Secondary data was used where 180 patients having colorectal cancer and receiving treatment in hospitals was recorded by nurses and doctors. Based on the results, fuzzy linear regression (Ni, 2005) is the best model to predict the tumor size developed by patients after receiving treatment in hospital.

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ICU in terms of Intensive Care Unit was established inMalaysia since 1968. The number of patients receiving treatment at ICU had been increased day by days until now. Rapid development of medical and surgical subspecialties in the last decade resulted in increasing demands for more ICU beds and provides momentum for its development. This study aims to identify the determinants of patient status (alive or died) after receiving treatment in ICU. Secondary data of 1314 patients who received ICU treatment recorded by nurses and doctors using cluster sampling was used. Binary logistic regression was applied in order to identify the determinants of patient status. Based on the results of the logistic regression, this study discovered that patient's age, days of patients in ICU, comorbid diseases and patient's discharge score are the factors contributing patients to die after received treatment in ICU.

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