TOTAL STUDENT EXPENDITURE ANALYSIS BASED ON INCOME AND MONTHLY NEEDS USING LOGISTIC REGRESSION

Abstract

A binary logistic regression analysis was performed to analyse the most impacting variables on total student expenditure from a random sample of college student data gathered from a private survey done by the authors. The specific target of this research is to know whether students are prodigal based on Jim Rohn’s budgeting strategy. The variables used as predictors are: a) gender, b) food, c) fashion, d) credit, e) hangout, f) assignment, g) electricity, h) laundry, i) training, j) others. The result of logistic regression analysis shows that a model with predictors of food, training, electricity, and others gives the best result with others expenses as the most significant factors. The data shows that females are more likely to shop more than males, while male students spend more money to hangout. Overall expenditure shows male are more prodigal than females. it is correctly classified in 76% of the cases. The key finding of this research is that the selected variables have significant effect on total student expenditure.