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Evaluation and Validation of Results
Published in Neeraj Kumar, Aaisha Makkar, Machine Learning in Cognitive IoT, 2020
It is very important to understand the relationship among the variables. There can be the number of correlation among the variables such as one variable completely depends upon on the other, or the two variables depend upon the third variable, and so on. The value of this parameter mainly lies from -1 to 1. Depending upon this value, we can categorize the correlation into the following: Strong correlation: If the value of correlation lies near to a positive one, then the variables are strongly correlated.Negative correlation: If the value of correlation lies near the negative one, this means the variables are negatively correlated.Weak correlation: If the value of correlation lies near to zero, this implies the weaker correlation.No correlation: If the value of correlation is zero, this means there exists no relation at all.
Data Analysis of Lead in Soil (HUD Survey Data)
Published in Joseph J. Breen, Cindy R. Stroup, Lead Poisoning, 2020
S. F. Brown, B. D. Schultz, R. P. Clickner, S. Weitz
Correlations are often used to identify relationships between variables. If one variable causes, or directly affects, another in a linear manner, the two variables will be correlated. Although causation usually implies correlation, correlation does not imply a causal relationship. Even if there is a causal relationship, correlations cannot be used to determine which variable is the cause and which the effect. In many cases significant correlations are associated with a third, perhaps unmeasured, variable that affects the two correlated variables.
Research Methods in Human Factors
Published in Robert W. Proctor, Van Zandt Trisha, Human Factors in Simple and Complex Systems, 2018
Robert W. Proctor, Van Zandt Trisha
The coefficient r is always between −1.0 and +1.0. When X and Y are uncorrelated, r will equal 0. When r is 0, there is no linear relationship between X and Y. When they are perfectly correlated, r will equal +1.0 or −1.0, and the values of one variable can be related to the values of the other variable by a straight line. A positive correlation means that, as values of X increase, so do values of Y; a negative correlation means that, as values of X increase, those of Y decrease. Figure 2.5 provides illustrations of data for several values of r. Note that X and Y may be related to each other and still be uncorrelated. Because r only measures linear relationships, if X and Y are nonlinearly related, say, Y = X2, the correlation may be zero. Another useful statistic is r2, which gives the proportion of total variance that can be traced to the covariance of the two variables. It is often said that r2 reflects the amount of variance “explained” by the linear relationship.
State of Health Estimation of Lithium-Ion Batteries based on the CC-CV Charging Curve and Neural Network
Published in IETE Journal of Research, 2023
Ali Ghasemi Siani, Mehdi Mousavi Badjani, Hadi Rismani, Mojtaba Saeedimoghadam
It should be noted that these indices could be used to model SOH batteries when correlated with SOH batteries. To prove this, Pearson correlation coefficient is used according to relation 6. where and are examples of variables and and are the mean values of each. As shown in Table 1, there is a significant linear relationship between these HIs. As the correlation coefficient is closer +1 or −1, the correlation between the variables become more positive (+1) or negative (−1). The positive correlation means that if the values of one variable are increasing, the values in the other variable will increase as well. A correlation coefficient close to 0 indicates a weak correlation. Since the correlation between the extracted HIs and the battery capacity is very close to 1, these HIs can be used to estimate SOH.
Grit, motivational belief, self-regulated learning (SRL), and academic achievement of civil engineering students
Published in European Journal of Engineering Education, 2022
Hector Martin, Renaldo Craigwell, Karrisa Ramjarrie
The Pearson product-moment or bivariate correlation expresses the strength of the relationship between two variables (George and Mallery 2011). Correlation values range from −1 to +1, where the magnitude of the value indicates the strength of the relationship and the direction (negative or positive) reflects the relationship’s nature. A correlation coefficient of zero indicates no relationship between the variables at all. The assumption of using this method is that the two measured variables are approximately normally distributed (George and Mallery 2011). The Durbin-Watson statistic is used to test the remainder of the assumptions for sample suitability for parametric evaluation and to ensure the residuals are independent (or uncorrelated). This statistic can vary from 0 to 4, with the optimal being 2. All values were within the range of 1 and 3, rendering the analysis valid (Stevens 2012). Cook’s Distance statistic for each participant was determined. No value was over 1 to indicate significant outliers, which may place undue influence on the model. Scatter plots were also examined.
The spatial association of social vulnerability with COVID-19 prevalence in the contiguous United States
Published in International Journal of Environmental Health Research, 2022
Chuyuan Wang, Ziqi Li, Mason Clay Mathews, Sarbeswar Praharaj, Brajesh Karna, Patricia Solís
There are four limitations to this study. First, correlation does not imply causation. Our results only indicate the statistical relationship between SVI and COVID-19 prevalence in the contiguous US, but do not imply the potential cause–effect relationship between these two variables. Second, the results of a locally weighted correlation analysis depend on a pre-defined bandwidth or the number of neighboring features. The selection of a bandwidth is arbitrary and based on researchers’ experiences. A change of the bandwidth can result in changes in results. Third, we did not perform significance tests for local Spearman’s ρ because, to the best of our knowledge, there is no software that can compute local p-values for locally weighted correlation analysis. Fourth, the quality of COVID-19 data influences the accuracy of the results. There are some uncertainties within the national level COVID-19 data set because counties report COVID-19 cases and deaths using different methods or definitions. Future studies can use a wider selection of social, demographic, economic, and health indicators to perform a comprehensive analysis between vulnerability factors and COVID-19 prevalence for the entire United States or other countries around the world.