• The Korean Journal of Community Living Science
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• v.15 no.3
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• pp.167-177
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• 2004

This study is designed to understand purchase motivation for garment depending on demographic factors among college students in Yanbian, China. Questionnaire was used for measurement tools to study the subject of the thesis. The main study was conducted against 450 college students from May 17 to June 5, 2001. The data for the study were analyzed using SAS PC program for frequency distribution, percentage, t -test, and one way ANOVA. The purchase motivation for garment are affected by demographic factors such as gender, average monthly household income, monthly expense for clothing. The result was showed as follows: A meaningful difference showed in 3 areas 'to try a new trend, impulsive buying at the store display, discount advertising' depending on the gender in terms of purchase motivations, and in all the three areas, male students showed a higher ranking. But in other motivation areas, no difference was noticed in terms of gender. In terms of purchase motivation based on monthly income, only one area 'impulse buying from a store display' showed a meaningful difference. Respondents with an average monthly household income above 2,000 yuan showed a higher tendency of 'impulse buying' compared to those with below 500 yuan or those with between 500-2,000yuan. Those with the average monthly household income below 500 yuan showed the lowest ranking in the impulse buying. In other areas of purchase motivation, average monthly household income was not an important element. A meaningful difference showed in 4 areas, 'to try a new trend, impulse buying from a store display, discount advertising', and 'for a change of mood' in the product motivation based on expense on clothing. Respondents with an average monthly expense for clothing above 100 yuan showed a higher ranking in all 4 areas than those with less than 100 yuan. In other areas, the average monthly clothing expense didn't give any impact.

• Journal of the Korean Regional Science Association
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• v.31 no.2
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• pp.63-77
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• 2015

Effects of shifting trend from Chonsei to monthly-rent and the changes in rental housing prices upon the welfare of tenants has been analyzed. In 2014, welfare of tenants has been found to be decreased by 660 billion Korean Won due to the change in rental housing prices. From the point of view of the welfare of tenants, it has been found that monthly-rent stabilizing policies are 1.47 times more effective than Chonsei price stabilizing policies. The effect of monthly-rent stabilizing policies has been found to be increased with the increase in the proportion of monthly-rent transaction, the decrease in the speed of the shifting trend from Chonsei to monthly-rent, the decrease in interest rate, the decrease in the proportion of variable deposit in monthly-rent, the decrease in the proportion of debt in Chonsei deposit. These findings indicate that the relative importance of monthly-rent stabilizing policies will be increased in the future.

• Journal of Environmental Science International
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• v.28 no.6
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• pp.571-579
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• 2019

In this study, monthly average values of BOD, COD, and TOC observed for 10 years (2008-2017) in the Nam River were estimated, and monthly variations of BOD, COD, and TOC were analyzed. Monthly average COD was always higher than monthly average BOD; monthly average TOC was high from June to September when rainfall was high. Monthly correlation coefficients between BOD and COD ranged from 0.57 to 0.94, those between BOD and TOC from 0.45 to 0.93, and those between COD and TOC from 0.75 to 0.93. The correlation coefficients were high from November to February when rainfall was low. Regression analyses for monthly average water quality data of the Nam River classified into dry season (October to April) and wet season (May to September) were conducted. Correlation coefficients were higher in the dry season than those in the wet season, and the determination coefficients of linear regression functions for BOD and COD with TOC were also higher in the dry season than those in the wet season. From this study, it can be concluded that it is appropriate to use monthly data to analyze the correlations among BOD, COD, and TOC in the stream. To analyze the relationship between TOC flowing into the stream and BOD/COD, it was found that seasonal characteristics should be considered.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.239-242
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• 2003

The purpose of this study is to estimate monthly evapotranspiration (ET) using normalized difference vegetation index (NDVI) from NOAA/AVHRR Korea peninsula images. Morton actual ET for land surface conditions was evaluated by using 73 daily meteorological data, and the monthly averaged Morton ETs for each land cover were compared with the monthly maximum NDVIs of a year, 2001. There was a high correlation between monthly maximum NDVI and monthly averaged Morton ET. It was concluded that the monthly ET can be estimated from the NDVI information of NOAA/AVHRR.

• The Korean Journal of Community Living Science
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• v.15 no.4
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• pp.67-78
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• 2004

This study was designed to understand purchase motivation for clothing, evaluation criteria for clothing, evaluation criteria for stores depending on average monthly expense for clothing among female college students in Yanbian, China. Questionnaire was used for measurement tools to study the subject of the thesis. Each question was rated in 5 point scale, where 1 means 'not at all' and 5 means 'definitely'. The main study was conducted against 300 female college students from May 17 to June 5, 2001. The data for the study were analyzed using SAS PC program for frequency distribution, percentage, t -test. The purchase motivation for clothing, evaluation criteria for clothing, evaluation criteria for stores are affected by average monthly expense for clothing. The result was showed as follows : A significant difference was showed in the purchase motivation, evaluation criteria for clothing, evaluation criteria for stores, preferred stores between lower group and higher group depending on monthly expense on clothing. A meaningful difference was showed in 4 areas'to try a new trend, impulsive buying at the store display, for a change of mood, for stylish appearance'in the purchase motivations for clothing depending on the monthly expense for clothing, in 2 areas'brand, trendy fashion'in evaluation criteria for clothing depending on the monthly expense for clothing, in 3 areas 'after service, hospitality of sales person, brand awareness' in the evaluation criteria for stores depending on the monthly expense for clothing, And Respondents of a high expense group for clothing showed higher means compared to a low expense group for clothing in all areas. A significant difference was showed among groups regarding preferred stores based on the monthly expense on clothing.

• Journal of the Korean housing association
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• v.26 no.1
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• pp.71-79
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• 2015

This study was aimed to explore housing costs and housing cost burdens of early-career young renter households in Seoul Metropolitan area. Between March 10 and April 24, 2014, an online questionnaire survey was conducted to young professionals in Seoul Metropolitan area living apart from their parents. Among the responses collected, this study analyzed 476 useable responses from renter households. Major findings were as follows: (1) Jeon-se renters' deposit was average 2.24 times their annual income; deposit of monthly renters with deposit was average 6.7 times their monthly income and their monthly rent was 18.6 percent of their monthly income; and monthly rent of monthly renters without deposit was about 23 percent of their monthly income; (2) thirty percent of the respondents were found to have housing cost burden paying 25 percent or more of their income for rental costs; and (3) about 55 percent received parental supports to pay current housing costs. The study findings implies that it is critical to extend provision of affordable housing units with small or no deposit for beginning-stage professionals.

• Water for future
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• v.13 no.4
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• pp.41-50
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• 1980

The prupose of this study are the theoretical examination of Gamma distribution function and its application to hydraulic engineering, that is studying the simulation of monthly streamflow by the Gamma distribtution function model(Gamma Model) based on Monte Carlo technique. In the analysis, monthly streamflow data in the Nak Dong River, the Han River, and the Keum River were used and the data were changed to modular coefficient in order to make the analysis convenient. At first, the fitness of monthly streamflow to 2-Parameter Gamma distribution was tested by Chi-square and Kolmogrov-Smironov test, by which it was found the monthly streamflow were fit well to this Gamma distribution function. Then, the Gamma Model based on the Gamma distribution and Monte Carlo Method was used in the simulation of monthly streamflow, and simulateddata showed that all their stastical characteristics were preserved well in the simulation. Consequently, it can be concluded that the Gamma Model is suitable for the simulation of monthly streamflow series directly by using the Mote Carlo technique.

• Journal of Climate Change Research
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• v.7 no.4
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• pp.507-512
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• 2016

This study was conducted to classify agroclimatic zones in South Korea. To classify the agroclimatic zones, such climatic factors as amount of rainfall from April to May, amount of rainfall in October, monthly average air temperature in January, monthly average air temperature from April to May, monthly average air temperature from April to September, monthly average air temperature from December to March, monthly minimum air temperature in January, monthly minimum air temperature from April to May, Warmth Index were considered as major influencing factors on the crop growth. Climatic factors were computed from monthly air temperature and precipitation of climatological normal year (1981~2010) at 1 km grid cell estimated from a geospatial climate interpolation method. The agroclimatic zones using k-means cluster analysis method were classified into 6 zones.

• Korean Journal of Hydrosciences
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• v.3
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• pp.105-113
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• 1992

Various modeling approaches to study a long term behavior of streamflow or groundwater storage have been conducted. In this study, a Multivariate AR (1) Model has been applied to generate monthly flows of the one key station which has historical flows using monthly flows of the three subordinate stations. The Model performance was examined using statistical comparisons between the historical and generated monthly series such as mean, variance, skewness. Also, the correlation coefficients (lag-zero, and lag-one) between the two monthly flows were compared. The results showed that the modeled generated flows were statistically similar to the historical flows.

• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.