IJARCCE ISSN (Online) 2278-1021 ISSN (Print) 2319-5940 International Journal of Advanced Research in Computer and Communication Engineering Vol. 8, Issue 4, April 2019 Copyright to IJARCCE DOI 10.17148/IJARCCE.2019.8401 1 Correlation and Linear Regression Analysis (CALRA): A Predictive Decision Support System for LEAE Domingo V. Origines Jr 1 , Dr. Meliza P. Alo 2 Information Technology Department, SPAMAST, Matti, Digos, Davao Del Sur, Philippines 1 Professor, Graduate School, Southern Philippines, Agri-business, Marine, and Aquatic School of Technology (SPAMAST), Matti, Digos, Davao Del Sur, Philippines 2 Abstract: This study introduce a Decision Support System called Correlation and Linear Regression Analysis – Decision Support System (CALRA-DSS) System. The CALRA-DSS will use Data mining technique in the process of discovering knowledge which in turn can be used to predict future results. CALRA-DSS predicts Students chances whether passed or Failed in the LEAE Licensure Examination. The integration of Data Mining Technique using Pearson-Product Moment Correlation that is used to determine the degree to which two variables are related and Regression Analysis that is used to examine the relationship between and one dependent and one independent variable. The data to be tested by this CALRA-DSS were the April 2016 Agricultural Engineering graduates of ICET, SPAMAST- Digos Campus who participated in the LEAE Mock Board Examination last and took the August 2016 LEAE. The academic records of these graduates were taken from the SPAMAST Electronic Students Information System (eSMS) Digos Campus while the Mock Board data Result was taken from the SPAMAST LEAE Reviewer Committee. It is concluded that with the use of this tool, the ICET Department can implement an intervention program timely before Student would intend to take the LEAE. Based on the Outcome, the results obtained from the Correlation and Regression Analysis and the attributes obtained from eSMS, the identified Academic Predictors has a strong correlation to Mock Board Examination. In general outcome of the study can give a hint to the Students as to which subjects can be considered as predictor variables for their licensure exam scores and hence become their focus of study/review while still studying. Keywords: Correlation, Data Mining, Regression, LEAE, Academic Performance, Prediction. I. INTRODUCTION Decision support systems (DSS) are defined as interactive application systems intended to help decision makers utilize data and models in order to identify problems, solve problems and make decisions. The mission of decision support systems is to improve effectiveness, rather than the efficiency of decisions [21]. This study introduce a Decision Support System called Correlation and Linear Regression Analysis–Decision Support System (CALRA-DSS) System, a system tool that can process and discover a knowledge which in turn can be used to predict future results. Using Academic Predictors, the CALRA-DSS can predict Students chances whether passed or Failed in the Licensure Examination. The integration of Data Mining Technique to the system using Pearson-Product Moment Correlation that is used to determine the degree to which two variables are related and Regression Analysis that is used to examine and predict the relationship between one dependent and one independent variable. Basically, regression takes a numerical dataset and develops a mathematical formula that fits the data. After performing an analysis, the regression statistics used to predict the dependent variable when the independent variable is known [6]. The integration allows the use of more than one input variable and allows for the fitting of more complex models basis for Decision Support System that strongly predicts the relationship between academic variables and result in the Mock Board Examination[9]. In an Academic Institution using Decision Support System, there are several ways of defining the quality of higher education institutions (HEI) in the Philippines. One tangible measure commonly used in the country is the performance of an HEI’s graduates in state licensure examinations [7]. There have been several attempts to discover models in predicting the performance in licensure examination but most studies recommend for an extensive study covering other independent variables and other approaches [8]. Garciano found out that the Academic Performance such as General Education Subjects, Agricultural Engineering (AE) Major Subjects, and 80% Score Performance on the Mock Board Examination has a strong correlation in achieving a