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Statistics
Published in Dušan Teodorović, Miloš Nikolić, Quantitative Methods in Transportation, 2020
Dušan Teodorović, Miloš Nikolić
In cases when the values of one variable increase when the corresponding values of the second variable decrease, we talk about a negative relationship. Experiences from several cities show that the number of vehicles entering the downtown (business)district decreases when the congestion pricing fee increases. In this case, there is a negative relationship between the number of vehicles entering the central business district and the congestion pricing fee.
Case-Specific Observations: Deriving Evidence
Published in Susan B. Norton, Susan M. Cormier, Glenn W. Suter, Ecological Causal Assessment, 2014
Susan B. Norton, David Farrar, Michael Griffith
Correlations (see Table 10.3) provide a dimensionless expression of covariation. Results range from −1 to 1 with values of +1 indicating a perfect positive relationship (i.e., both variables increase or decrease in tandem), values of −1 indicating a perfect negative relationship (i.e., the two variables increase or decrease in opposite directions), and 0 indicating no association.
Fuzzy Logic-Based Approaches for Estimating Efforts Invested in Component Selection
Published in Kirti Seth, Ashish Seth, Aprna Tripathi, Component-Based Systems, 2020
Kirti Seth, Ashish Seth, Aprna Tripathi
The strength of linear association among two variables will be calculated. Most of the time the correlation will be between −1.0 and +1.0. The positive relationship will be obtained in cases of positive correlation. The negative relationship will be obtained in cases of negative correlation.
Reforming premium amount in the Indonesian National Health Insurance System program using system dynamics
Published in Cogent Engineering, 2021
Diva Kurnianingtyas, Budi Santosa, Nurhadi Siswanto
The structure of the CLD is based on some of the literature in the previous section. The structure has two main components, namely negative feedback (balancing loop) to maintain a balanced relationship and positive feedback (reinforcing loop) to strengthen the relationship (Dulac et al., 2005). A positive relationship is a relationship structure that gives an interaction to oneself to produce an increase or decrease. A positive relationship is indicated by a “+” sign, which means that the variable will change in the same direction. For example, if the first variable decreases, then the second variable also decreases. The relationship will result in improvement, increase deviation, and strengthen change (Sterman, 2010). Conversely, a negative relationship is indicated by a “-” sign, which means the value of the variable will change in the opposite direction. The description of the CLD of the INHIS system, as shown in Figure 5, is explained below. The CLD has four reinforcing loops (loops 1, 6, 7, and 9) and balancing loops (loops 2, 3, 4, 5, and 8). As a reminder, in this study, variable writing will be written in underline.
An ergonomics-driven QFD model to improve medical laboratory staff and patient satisfaction
Published in Theoretical Issues in Ergonomics Science, 2022
Amer M. Momani, Tasneem Al-Shaikh, Ahmad Abdelhafiz Mumani, Omar Al-Araidah
The interrelation between the “HOWs” and how strong these relations are is determined by researching the ergonomic design elements’ impact on each other and past experience, using experts’ opinions, engineering analysis, best practices, and online resources(i.e., websites, literature review, reports). Rate the relationships as either strongly positive, somewhat positive, negative, strongly negative, or non-existent. A positive relationship is one in which an increase in one will cause an increase in the other, while a negative relationship is one in which an increase in one causes a decrease in the other.
Multimodal face shape detection based on human temperament with hybrid feature fusion and Inception V3 extraction model
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Srinivas Adapa, Vamsidhar Enireddy
The correlation coefficient is the difference between the compressed and original images. The PCC is also defined or termed as Pearson R statistical test. The PCC ranges values between −1 and 1. If the value of correlation is −1, then it specifies a strong negative relationship. If it is 0, then it indicates no relationship. If it is 1, it specifies a strong positive relationship. The mathematical expression of PCC is defined in Equation (22). The graphical illustration of PCC metrics is shown in Figure 11.