• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.1
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• pp.57-66
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• 2001

This study used four theoretical models, such as two-point linear model, linear regression model, quadratic regression model and cubic regression model which are presented from The Ministry of Science and Technology, for extraction of urban surface temperature from Landsat TM band 6 image. Through correlation and regression analysis between result of four models and AWS(automatic weather station) observation data, this study could verify spatial distribution characteristic of urban surface temperature using GIS spatial analysis method. The result of analysis for surface temperature by landcover showed that the urban and the barren land belonged to the highest surface temperature class. And there was also -0.85 correlation in the result of correlation analysis between surface temperature and NDVI. In this result, the meteorological environmental characteristics wuld be regarded as one of the important factor in urban planning.

• Journal of Sasang Constitutional Medicine
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• v.33 no.3
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• pp.127-137
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• 2021

Objectives The purpose of this study was to identify the effects of personality by each Sasang constitution on sleep using Positive Affect and Negative Affect Schedule(PANAS) and NEO-personality inventory(NEO-PI). Methods The subjects of this study were 2,078 people who had information on Sasang constitutional type and personality(PANAS, NEO-PI) among the data established at the Korean Medicine Data Center. Sleep time and sleep quality were investigated through self-written questionnaires. Sleep time was collected by hand-writing, and sleep quality was checked on a 4 point Likert scale. Pearson correlation analysis was conducted to see the correlation between personality and sleep in each constitutional type. Logistic regression was performed using personality as independent variables to find out how much personality affects sleep time. In order to find out how much personality affects sleep quality, regression analysis was performed using personality as independent variables. Results & Conclusions Sleep time was hardly affected by personality. As a result of Pearson correlation analysis, sleep time in all subjects did not show a significant correlation with personality. In logistic regression on sleep time as the dependent variable, no statistically significant results were obtained except for the Negative Affect(NA) in Taeeumin. Sleep quality showed a statistically significant correlation with the negative affect(NA), neuroticism(N), extraversion(E), Physical Component Summary(PCS) and Mental Component Summary(MCS). As a result of regression analysis on sleep quality as the dependent variable, neuroticism(N), negative affect(NA), positive affect(PA), and extraversion(E) were found in the factors affecting sleep quality. Besides, how much personality affected sleep quality might differ in each constitution. In all constitutions, sleep quality was affected by N, but the rank of N was different in each constitution. The sleep quality of Soyangin was not affected by E, and the sleep quality of Taeeumin was specifically affected by O.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.367-374
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• 2005

This paper proposes a sound quality index to evaluate the vehicle interior noise. The index was developed using a correlation analysis of an objective measurement and a subjective evaluation data. First, the objective set of measurements was obtained at two specified driving conditions. One is from a wide-open test condition and the other is from a constant-speed test condition. At the same time, subjective evaluation was carried out using a score of ten scale where 17 test engineers participated in the experiment. The correlation analysis between the psycho-acoustic parameters derived from the objective measurement and the subjective evaluation was performed. The most critical factors at both test conditions were determined, and the corresponding equations for the sound quality were obtained from the multiple factor regression method. Finally, a comparative work between previous index and present index was performed to validate the effectiveness of the proposed index.

• Journal of Ecology and Environment
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• v.30 no.1
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• pp.39-47
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• 2007

The relationships between the cover of herbaceous species and 15 soil chemical properties (organic carbon contents, total N, available P, exchangeable K, Na, Ca and Mg, HCl-extractable Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) in nine poorly controlled waste landfill sites in Korea were examined by correlation analysis and multiple regression equations. Species showed different patterns of correlation between their cover values and soil chemical properties. The cover of Ambrosia artemisiifolia var. elatior, Aster subulatus var. sandwicensis and Erechtites hieracifolia were negatively correlated with the contents of Fe, Mn and Ni within landfill soils. Total cover of all species in quadrats was positively correlated with the contents of Cd and negatively correlated with the contents of Mn and Fe from stepwise regression analysis with 15 soil properties. Canonical correspondence analysis demonstrated that the distribution of native and exotic plants on poorly controlled landfills was significantly influenced by the contents of Na and Ca in soils, respectively.

• Journal of Korean Academy of Nursing
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• v.33 no.5
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• pp.609-617
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• 2003

Purpose: The purpose of this study is to understand the depression of patients for Fibromyalgia Syndrome(FMS) and to identify the factors influencing depression. Method: The instruments used here are Beck Depression Inventory in depression, the Korean Rheumatology Health Association' instruments in Self-Efficacy. Also, Pain and Fatigue was measured by Visual Graphic Rating Scale. The subject of study is 76 outpatients diagnosing FMS from rheumatism specialists at C hospital in D city. The data has been collected from Sep. 1st to Sep. 30th in 2001. For the analysis of collected data, frequency analysis, independent t-test, analysis of variance, Pearson's correlation and multiple regression analysis were used for statistical analysis with SAS statistical program. Result: General characteristics showing statistically significant difference in depression were age, education, occupation, gender, exercise and sleep in the patients with FMS. Depression for the patients with FMS has negative correlation coefficients with Self-efficacy and ADL, and positive correlation coefficients with Pain and Fatigue. The suitable regression form resulting from the multiple regression analysis to investigate the influencing factors of depression for the partients with FMS was expressed by y =50.067 - 0.278x$_1$ + 1.320x$_2$ (x$_1$: Self-Efficacy x$_2$: Fatigue) and $R^2$ =0.427. Conclusion: The factors influencing on depression of patients for FMS was Self-Efficacy, ADL, Pain, and Fatigue. Further study needs to be done identify methods of overcoming and presentation of depression in FMS.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.153-164
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• 1990

A statistical correlation between cloud thickness and brightness is shown by regression analysis using the least-square method. Cloud thicknesses are obtained from radiosonde observation. Brightness values are obtained from GMS visible channel. Regression analyses are preformed on both thickness data used in conjunction with brightness data for summer season. The results are shown by the regression curve relating thickness and brightness accounting for 79% of variance. And the relationship between thickness and precipitable water in the cloud layers is analyzed. The thickness shows a positive correlation with precipitable water in cloudy layers.

• Journal of Korean Society on Water Environment
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• v.39 no.2
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• pp.162-174
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• 2023

In order to prevent water pollution caused by organic matter, Total Organic Carbon(TOC) has been adopted indicator and monitored. TOC can be divided into Dissolved Organic Carbon(DOC) and Particulate Organic Carbon(POC). POC is largely precipitated and removed during stream flow, which making DOC environmentally significant. However, there are lack of studies to define spatio-temporal distributions of DOC in stream affected by various land use. Therefore, it is necessary to estimate the past DOC concentration using other water quality indicators to evaluate status of watershed management. In this study, DOC was estimated by correlation and regression analysis using three different organic matter indicators monitored in mixed land-use watersheds. The results of correlation analysis showed that DOC has the highest correlation with TOC. Based on the results of the correlation analysis, the single- and multiple-regression models were developed using Biochemical Oxygen Demand(BOD), Chemical Oxygen Demand(COD), and TOC. The results of the prediction accuracy for three different regression models showed that the single-regression model with TOC was better than those of the other multiple-regression models. The trend analysis using extended average concentration DOC data shows that DOC tends to decrease reflecting watershed management. This study could contribute to assessment and management of organic water pollution in mixed land-use watershed by suggesting methods for assessment of unmeasured DOC concentration.

• Journal of Environmental Science International
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• v.15 no.9
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• pp.887-895
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• 2006

Through this research of the analysis on the frequency flood discharges regarding basin property factors, a linear regression system was introduced, and as a result, the item with the highest correlation with the frequency flood discharges from Nakdong river basin is the basin area, and the second highest is the average width of basin and the river length. The following results were obtained after looking at the multi correlation between the flood discharge and the collected basin property factors using the data from the established river maintenance master plan of the one hundred twenty-five rivers in the Nakdong river basin. The result of analysis on multivariate correlation between the flood discharges and the most basic data in determining the flood discharges as basin area, river length, basin slope, river slope, average width of basin, shape factor and probability precipitation showed more than 0.9 of correlation in terms of the multi correlation coefficient and more than 0.85 for the determination coefficient. The model which induced a regression system through multi correlation analysis using basin property factors is concluded to be a good reference in estimating the design flood discharge of unmeasured basin.

• Proceedings of the Safety Management and Science Conference
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• 2010.11a
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• pp.539-550
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• 2010

The study presents the application strategy and understanding of regression analysis with GLM(Generalized Linear Model) unifying with other statistical techniques such as correlation analysis and design of experiment(DOE). The quidelines proposed in this paper can be used for practioners to implement GLM and ANOVA(Analysis of Variance) for the DMAIC 5 steps of six sigma breakthrough.

• Journal of Industrial Technology
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• v.26 no.A
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• pp.119-127
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• 2006

The purpose of this study is to present quantitatively the influence of variables that had the largest effect on the changes in suspended solids(SS), which would cause turbid water phenomenon, among water quality factors of the non-point pollution source, and then to develop a multiple regression equation of SS and predict the water quality of ungaged watersheds so as to provide basic data to establish efficient management plans for SS which flow in rivers and lakes. To identify the correlation of SS with the amount of rainfall and the state of land use, a simple correlation analysis and a simple regression analysis were conducted respectively. Finally, a multiple regression analysis was conducted to provide that SS were set as dependent variables while the amount of rainfall, paddy fields and dry fields were set as independent variables. As a result, the amount of rainfall had the most significant influence on changes in SS, followed by dry fields and paddy fields. In addition, the multiple regression equation was developed to predict SS in unmeasurable watersheds.