Estimates from confidence intervals are more powerful than point estimates, because there are intervals for parameter values used to estimate populations. In relation to global conditions, involving issues such as type 2 diabetes mellitus, it is very difficult to make estimations limited to one point only. Therefore, in this article, we estimate confidence intervals in a truncated spline model for type 2 diabetes data. We use a non-parametric regression model through a multi-variable spline linear estimator. The use of the model results from the irregularity of the data, so it does not form a parametric pattern. Subsequently, we obtained the interval from beta parameter values for each predictor. Body mass index, HDL cholesterol, LDL cholesterol and triglycerides all have two regression coefficients at different intervals as the number of the found optimal knot points is one. This value is the interval for multivariable spline regression coefficients that can occur in a population of type 2 diabetes patients.

Principal component analysis (PCA) is a multiple variable analysis method that aims to reduce the dimensions of the original variable, which are mostly correlated so that new variables that are not correlated are obtained. The data used is criminality data in Indonesia in 2016 which contains outlier data in it. Therefore this study cannot use classic PCA because classic PCA was formed based on a covariant variant matrix that is very sensitive to the existence of outlier data. To overcome this problem, PCA robust is used with the Modified One-Step M-Estimator method with a MADn scale estimator to get the main components that are not much influenced by outliers. Modified One-Step M-Estimator (MOM) is the average remaining value of all extreme values that have been issued. The results obtained are there are 3 main components that can explain 85.19% of the variance of the 7 original variables.