The main purpose of this study is to formulate a mathematical regression model showing the significant effect of the independent variables to the dependent variable. The researchers obtained the quarterly data from the Philippine Statistics Authority (PSA) limited only from 2001 to 2016 with a total of 448 observations. The data include the following factors: Employment Rate (X1), Underemployment Rate (X2), Gross National Income (X3), Household Population 15 yrs+ (X4) and Gross Domestic Product (X5) as independent variables that can affect the Labor Force Participation Rate in the Philippines. The researchers use the IBM Statistical Package for the Social Sciences as a statistical tool for recording and analyzing the quarterly data. The researchers employed two main statistical treatments which are subjected at 1% level of significance: Pearson R Correlation, to determine which of the independent variables have significant relationship with the dependent variable; and Multiple Linear Regression Analysis, to formulate the fitted mathematical model for the dependent variable. According to the results gathered, three out of five independent variables become the predictors of Labor Force Participation Rate which are included in the fitted model. Moreover, the researchers also utilize the Paired-T test to find if the predicted values and the actual values of the dependent variable have significant difference with each other