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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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Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 -2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1) 12 and SARIMA (0, 1, 1) (0, 0, 1) 12 models were found fit and confirmed by the Ljung-Box test and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.