logistic regression

Nowadays, improving quality is advocated as a strategy to increase market share, and failing to address this crucial issue results in exclusion from the competitive landscape. Most studies undertaken in recent years have investigated and optimized continuous response variables while ignoring categorical characteristics. This necessitates a change in statistical methods in this discipline to ones that take categorical responses into account. Statistical techniques have always provided researchers with estimates of parameters that are subject to uncertainty. Hence, considering uncertainty in modeling is essential for reducing errors and minimizing costs while increasing quality. In this study, we deal with the robust design of quality characteristics in categorical response problems to reach optimal levels of control variables, which can minimize the error caused by modeling and implementation and provide more accurate estimates. A portion of the uncertainty is considered while estimating the model parameters. However, the proposed approach assumes that the optimal settings of design variables during the implementation phase will also experience oscillations. Finally, in the optimization phase, multiple equations relating to response levels are modeled and solved using the goal programming approach. The results showed that our approaches could achieve solutions with robustness against the two main sources of errors.

Diagnosis of diseases is a critical problem that can help for more accurate decision-making regarding the patients’ health and required treatments. Machine learning is a solution to detect and understand the symptoms related to heart disease. In this paper, a logistic regression model is proposed to predict heart disease based on a dataset with 299 people and 13 variables and to evaluate the impact of different predictors on the outcome. In this regard, at first, the effect of each predictor on the precise prediction of the outcome has been evaluated and analyzed by statistical measurements such as AIC scores and p-values. The logit models of different predictors have also been analyzed and compared to select the predictors with the highest impact on heart disease. Then, the combined model that best fits the dataset has been determined using two statistical approaches. Based on the results, the proposed model predicts heart disease with a sensitivity and specificity of 84.21% and 90.38%, respectively. Finally, using normal probability density curves, the likelihood ratios have been established based on classes 1 and 0. The results show that the likelihood ratio classifier performs as satisfactorily as the logistic regression model.

Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity of planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties surrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic methods are extensive in production planning, they seem to be inefficient because of daily fluctuations in real factories. Decision support systems can provide productive tools for production planners to offer a feasible and prompt decision in effective and robust production planning. In this paper, we propose a robust decision support tool for detailed production planning based on statistical multivariate method including principal component analysis and logistic regression. The proposed approach has been used in a real case in Iranian automotive industry. In the presence of existing multisource uncertainties, the results of applying the proposed method in the selected case show that the accuracy of daily production planning increases in comparison with the existing method.