The contribution statistical test of categories to dependencies between qualitative variables is needed, because the existence of these categories will influence decision making. Decisions taken can be misleading, if a category does not contribute. If N is the matrix from 2×J or I×2 contingency table which is constructed from two qualitative variables, then the principal coordinates can be calculated using the simplification of correspondence analysis (SoCA). Principal coordinates produced by the SoCA are the same as conventional correspondence analysis (CA), but the SoCA presents a simpler calculation, because it is calculated directly from the elements of N. Principal coordinates derived from 2×J matrix N can be used to test the contribution of categories to dependencies between qualitative variables more simply, and obtained confidence intervals for each category.
Optimization is the process of getting an optimal response. The taguchi design is an experimental design that is often used to get robust responses. In the multi-response Taguchi design, the optimization process is carried out by considering all responses simultaneously. Cases study used in this research is aluminum production cases with four treatments, namely tool rotational speed, welding speed, shoulder tool diameter and pin diameter. Each treatment was tried with three levels. Optimized responses are tensile strength, average microhardness and average grain size.The purpose of this research is determining the point of optimization using response surface method and desirability function. The optimization results obtained are tool rotational speed of 1100 RPM, Welding speed of 40 mm/min, Shoulder diameter of 21 mm, and pin diameter of 5.4 mm.
In the last five years, poverty had been a major problem in West Java Province (WJP). Hence, it becomes a strategic development issue for the next five years. To contribute in eradicating the poverty, we aimed this study at identifying poverty factors and measuring their effects. There are two issues in modelling poverty in WJP. First, there was spatial dependency in poverty among regions. Second, due to data limitation, some important factors were not included in the empirical model. Both might lead to bias in regression parameter estimates. Accordingly, we applied a fixed effect panel spatial error model on the poverty rate in WJP. We found that health and economic welfare effected poverty.
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