The Effect of Perceived Supervisor–Subordinate Congruence in Honesty on Emotional Exhaustion: A Polynomial Regression Analysis

Do subordinates unequivocally prefer honest superv =isors? This study investigates whether congruence in supervisor–subordinate honesty has a positive effect on lowering the emotional exhaustion experienced by subordinates. For the research data, a two-part survey with a one-month time interval was conducted among office workers, and 409 responses were collected. These were empirically analyzed using polynomial regression analysis and response surface analysis, instead of the common methodology based on difference values used in past studies on the fit between a person and their environment. The analysis results confirmed that supervisor–subordinate congruence in honesty has a negative relationship with subordinates’ emotional exhaustion and supervisor–subordinate congruence at higher levels of honesty will have stronger negative relationships with emotional exhaustion. This study expands the intrapersonal context of the existing research on supervisors’ honesty to the interpersonal context and empirically demonstrates the effect of honesty congruence. It also discusses its theoretical and practical implications as well as limitations, and it provides suggestions for future studies.

Comparison between Multiple Regression Analysis, Polynomial Regression Analysis, and an Artificial Neural Network for Tensile Strength Prediction of BFRP and GFRP

In this study, multiple regression analysis (MRA) and polynomial regression analysis (PRA), which are traditional statistical methods, were applied to analyze factors affecting the tensile strength of basalt and glass fiber-reinforced polymers (FRPs) exposed to alkaline environments and predict the tensile strength degradation. The MRA and PRA are methods of estimating functions using statistical techniques, but there are disadvantages in the scalability of the model because they are limited by experimental results. Therefore, recently, highly scalable artificial neural networks (ANN) have been studied to analyze complex relationships. In this study, the prediction performance was evaluated in comparison to the MRA, PRA, and ANN. Tensile strength tests were conducted after exposure for 50, 100, and 200 days in alkaline environments at 20, 40, and 60 °C. The tensile strength was set as the dependent variable, with the temperature (TP), the exposure day (ED), and the diameter (D) as independent variables. The MRA and PRA results showed that the TP was the most influential factor in the tensile strength degradation of FRPs, followed by the exposure time (ED) and diameter (D). The ANN method provided the best correlation between predictions and experimental values, with the lowest error and error rate. The PRA method applied to the response surface method outperformed the MRA method, which is most commonly used. These results demonstrate that ANN can be the most efficient model for predicting the durability of FRPs.

A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis

In the manufacturing processes, process optimization tasks, to optimize their product quality, can be performed through the following procedures. First, process models mimicking functional relationships between quality characteristics and controllable factors are constructed. Next, based on these models, objective functions formulating process optimization problems are defined. Finally, optimization algorithms are applied for finding solutions for these functions. It is important to note that different solutions can be found whenever these algorithms are independently executed if a unique solution does not exist; this may cause confusion for process operators and engineers. This paper proposes a confidence interval (CI)-based process optimization method using second-order polynomial regression analysis. This method evaluates the quality of the different solutions in terms of the lengths of their CIs; these CIs enclose the outputs of the regression models for these solutions. As the CIs become narrower, the uncertainty about the solutions decreases (i.e., they become statistically significant). In the proposed method, after sorting the different solutions in ascending order, according to the lengths, the first few solutions are selected and recommended for the users. To verify the performance, the method is applied to a process dataset, gathered from a ball mill, used to grind ceramic powders and mix these powders with solvents and some additives. Simulation results show that this method can provide good solutions from a statistical perspective; among the provided solutions, the users are able to flexibly choose and use proper solutions fulfilling key requirements for target processes.

Assessing the effects of in vitro imposed water stress on pineapple growth in relation to biochemical stress indicators using polynomial regression analysis

Knowing the mechanisms that operate under water stress in commercial crops, particularly those that can affect productivity, such as phenolic or cell wall metabolism, is becoming increasingly important in a scenario of global climate change. However, our understanding of how to analyse statistically the relationships between these commonly used biochemical markers of water stress and growth in crops like pineapple, needs to be improved. In the present work, we have addressed the question of whether polynomial regression analysis can be used to describe the influence of selected plant metabolites (chlorophylls, carotenoids, phenolics and aldehydes) on shoot biomass, in response to a mannitol-induced water stress in temporary immersion bioreactors (TIBs). Polynomial regression analysis has been applied to investigate plant stress responses in many species but is very seldom used in in vitro screening studies. Here, the relationship between biochemical indicators (x; independent variable) and shoot growth (y; dependent variable) has been characterised, with y modelled as an nth degree polynomial in x. This statistical approach accommodated for the non-linear (curvilinear) relationships between variables, and the results showed that shoot biomass was negatively, and significantly correlated with soluble phenolics, cell wall-linked phenolics and other aldehydes (characterised by “High” R2 values).