• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.105.1-105.1
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• 2014

The study on linear correlation between auxiliary channels in LIGO data has been performed by using Pearson's correlation coefficient, generating a correlation map.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.1043-1052
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• 2009

To derive easily the coefficient of permeability from several other soil properties, the estimation model of coefficient of permeability was proposed using linear regression analysis. The coefficient of permeability is one of the major factors to evaluate the soil characteristics. The study area is located in Kangwon-do Pyeongchang-gun Jinbu-Myeon. Soil samples of 45 spots were taken from the study area and various soil tests were carried out in laboratory. After selecting the soil factor influenced by the coefficient of permeability through the correlation analysis, the estimation model of coefficient of permeability was developed using the linear regression analysis between the selected soil factor and the coefficient of permeability from permeability test. Also, the estimation model of coefficient of permeability was compared with the results from permeability test and empirical equation, and the suitability of proposed model was proved. As the result of correlation analysis between various soil factors and the coefficient of permeability using SPSS(statistical package for the social sciences), the largest influence factor of coefficient of permeability were the effective grain size, porosity and dry unit weight. The coefficient of permeability calculated from the proposed model was similar to that resulted from permeability test. Therefore, the proposed model can be used in case of estimating the coefficient of permeability at the same soil condition like study area.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.107.2-107.2
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• 2014

The study on non-linear correlation between auxiliary channels in LIGO data has been performed by using mutual information coefficient, generating a correlation map.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1306-1310
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• 2009

The roughness coefficients were estimated by the Manning's equation for the measured stage and flow velocity of Bocheong stream basin in Kum river. The relationships between the estimated roughness coefficients and the geomorphologic factors were formulated by the linear, logarithmic, exponential and power type function, thereafter correlation equations were presented. The correlation analysis was performed between the measured stream length and the basin area of Bocheong stream basin by the linear, logarithmic, exponential and power type function, and correlation equation for the stream length was given. The roughness coefficient has strong correlationship with stream slope, but low correlation coefficients with stream length and basin area. For the correlationship with the roughness coefficients and the stream slope, the logarithmic type function has the smallest correlation coefficient, on the other hand, the exponential type function has the largest correlation coefficient. For the relationship between the stream length and the basin area, the correlation coefficient of the logarithmic type function shows the smallest value, linear type function shows the largest value.

• Investigative Magnetic Resonance Imaging
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• v.21 no.3
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• pp.171-176
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• 2017

Purpose: To compare different MR sequences for quantification of gadolinium concentration. Materials and Methods: Gadolinium contrast agents were diluted into 36 different concentrations. They were scanned using gradient echo (GRE) and ultrashort echo time (UTE) and R1, $R2^*$ and phase values were estimated from collected data. For analysis, ROI masks were made for each concentration and then ROI value was measured by mean and standard deviation from the estimated quantitative maps. Correlation analysis was performed and correlation coefficient was calculated. Results: Using GRE sequence, R1 showed a strong linear correlation at concentrations of 10 mM or less, and $R2^*$ showed a strong linear correlation between 10 to 100 mM. The phase of GRE generally exhibited a negative linear relationship for concentrations of 100 mM or less. In the case of UTE, the phase had a strong negative linear relationship at concentrations 100 mM or above. Conclusion: R1, which was calculated by conventional GRE, showed a high performance of quantification for lower concentrations, with a correlation coefficient of 0.966 (10 mM or less). $R2^*$ showed stronger potential for higher concentrations with a correlation coefficient of 0.984 (10 to 100 mM), and UTE phase showed potential for even higher concentrations with a correlation coefficient of 0.992 (100 mM or above).

• Journal of Veterinary Clinics
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• v.27 no.4
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1563-1571
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• 2003

A fast response NO analyzer was applied to investigate the relation between cycle-by-cycle NO emissions and combustion chamber pressure. NO emissions were sampled at an isolated exhaust manifold of 4-stroke spark ignition engine to avoid the interference of exhaust gas from other cylinders. The linear correlation analysis was performed with collected data of NO emissions and combustion chamber pressure with respect to the various air-fuel mixture ratios and engine loads. The sampled data sets were obtained during 200 cycles at each operating condition. The results showed that there was a typical pattern in NO emissions from an exhaust port through a cycle. It was possible to set a block of crank angle in which the linear correlation coefficient between NO emissions and combustion chamber pressure was high. As the engine load increased, NO emissions were more dependent on combustion chamber pressure after TDC. It was also analyzed that the correlation between two parameters with respect to air-fuel mixture ratio tended to increase as mixture went leaner. Furthermore, this correlation coefficient for the mixture near the lean limit seemed to be kept high even though combustion was unstable.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.228-239
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• 2022

Many countries around the world using ICT technologies have various technologies to keep pace with the 4th industrial revolution, and various algorithms and systems have been developed accordingly. Among them, many industries and researchers are investing in unmanned automation systems based on AI. At the time when new technology development and algorithms are developed, decision-making by big data analysis applied to AI systems must be equipped with more sophistication. We apply, Pearson's correlation analysis is applied to six independent variables to find out the job satisfaction that office workers feel according to their job characteristics. First, a correlation coefficient is obtained to find out the degree of correlation for each variable. Second, the presence or absence of correlation for each data is verified through hypothesis testing. Third, after visualization processing using the size of the correlation coefficient, the degree of correlation between data is investigated. Fourth, the degree of correlation between variables will be verified based on the correlation coefficient obtained through the experiment and the results of the hypothesis test

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.1951-1966
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• 2020

Recent advancements in mobile device technology have enabled real-time positioning so that mobile patterns of people and favorable locations can be identified and related researches have become plentiful. One of the fields of research is the relationship between the object properties and the favored location to visit. The object properties of a person include personality, which is a major property jobs, income, gender, and age. In this study, we analyzed the relationship between the human personality and the preference of the location to visit. We used Spearman's Rank correlation coefficient, one of the many methods that can be used to determine the correlation between two variables. Instead of using actual data values, Spearman's Rank correlation coefficient deals with the ranks of the two data sets. In our research, the personality and the location data sets are used. Our personality data is ranked in five ranks and the location data is ranked in 8 ranks. Spearman's Rank correlation coefficient showed better results compared to Pearson linear correlation coefficient and Kendall rank correlation coefficient. Using Spearman's correlation coefficient, the degree of the relationship between the personality and the location preference is found to be 43%.

• Journal of Astronomy and Space Sciences
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• v.29 no.3
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• pp.259-267
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• 2012

Polar cap potential has long been considered as an indicator for the amount of energy flowing in the magnetosphere-ionosphere system. Thus, the estimation of polar cap potential is important to understand the physical process of the magnetosphere. To estimate the polar cap potential in the Northern Hemisphere, merging electric field by Kan & Lee (1979) is adopted. Relationships between the PC index and calculated merging electric field ($E^*$) are examined during full-time and storm-time periods separately. For this purpose Dst, AL, and PC indices and solar wind data are utilized during the period from 1996-2003. From this linear relationship, polar cap potential (${\Phi}^*$) is estimated using the formula by Doyle & Burke (1983). The values are represented as $58.1{\pm}26.9$ kV for the full-time period and $123.7{\pm}84.1$ kV for a storm-time period separately. Considering that the average value of polar cap potential of Doyle & Burke (1983) is about 47 kV during moderately quiet intervals with the S3-2 measurements, these results are similar to such. The monthly averaged variation of Dst, AL, and PC indices are then compared. The Dst and AL indices show distinct characteristics with peaks during equinoctial season whereas the average PC index according to the month shows higher values in autumn than in spring. The monthly variations of the linear correlation coefficients between solar wind parameters and geomagnetic indices are also examined. The PC-AL linear correlation coefficient is highest, being 0.82 with peaks during the equinoctial season. As with the AL index, the PC index may also prove useful for predicting the intensity of an auroral substorm. Generally, the linear correlation coefficients are shown low in summer due to conductance differences and other factors. To assess the role of the PC index during the recovery phase of a storm, the relation between the cumulative PC index and the duration is examined. Although the correlation coefficient lowers with the storm size, it is clear that the average correlation coefficient is high. There is a tendency that duration of the recovery phase is longer as the PC index increases.