Tag Archives: Binary logistic model

Statistical Modelling of Road Traffic KSI Car Accidents in England (STATS19) (Published)

Several generalised linear models for counts (i.e., Poisson Model) as well as for binary response (i.e., Binary Logistic Model) and ordinal response (i.e., Ordinal Logistic Model) depending on selected multiple explanatory factors (discrete/ categorical) were developed for the road KSI car accidents in England based on STATS19 data (that were manipulated and several new factors were created), after exploratory exploration of discrete/ dichotomous/ nominal/ ordinal factors applied graphical EDA techniques followed by univariate ANOVA/ ANCOVA as well as MANOVA/ MANCOVA based on same selected multiple explanatory factors. Only the main effects as well as two-way interactions were investigated. Majority of main effects and several interaction effects in GLM models were found statistically significant with greater or lesser likelihood of having consequences. The statistically significant KSI car accident factors were identified and quantified for leading to aims to reduce as well as to prevent the car accident, particularly the killed or seriously injured car accidents. It also leads to inform the policymakers on how best to reduce the number and severity of car crashes.

Keywords: ANOVA/ ANCOVA, Binary logistic model, Generalised Linear Modelling, KSI Car Accident, MANOVA/ MANCOVA, Ordinal Logistic Model, Poisson Multiple Model

The Application of the Binary Logistic Model: A Case of Joint Stock Commercial Bank for Investment and Development of Vietnam (Bidv) in Vinh Long Province (Published)

This study aims to estimate the factors affecting the probability of repayment of individual customers, corporate customers at BIDV in Vinh Long province. The sample data includes 403 individual customers and 160 corporate customers who selected from the BIDV’s customer data set. The regression analysis results tested seven factors affecting the probability of debt repayment of individual customers with significance level 0.10 from 9 factors proposed. Besides, five factors affecting the probability of debt repayment of corporate customers with significance level 0.05 from 8 factors proposed.

Keywords: BIDV, Binary logistic model, Customers, Debt, HVUH.