• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.73-81
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• 2013

Ordinary least squares (OLS) regression is the primary statistical method in previous studies for vegetation distribution patterns in relation to landform. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between NDVI (Normalized Difference Vegetation Index) patterns and changing trends of landform (elevation, slope) and solar intensity (insolation and duration of sunshine) in Mt Geum-gang of North-Korea. Results denoted that GWR was more powerful than OLS in interpreting relationships between NDVI patterns and landform/solar intensity, since GWR was characterized by higher adjusted R2, and reduced spatial autocorrelations in model residuals. Unlike OLS regression, GWR allowed the coefficients of explanatory variables to differ by locality by giving relatively more weight to NDVI patterns which are affected by local landform and solar factors. The strength of the regression relationships in the GWR increased significantly, by showing regression coefficient of higher than 70% (0.744) in the southern ridge of the experimental area. It is anticipated that this research output will serve to increase the scientific and objective vegetation monitoring in relation to landform and solar intensity by overcoming serious constraints suffered from the past non-GWR-based approach.

• Polymer(Korea)
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• v.29 no.5
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• pp.451-456
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• 2005

In the field of designing of nano-fillers of polyimide nanocomposites, the two strategic points are the heat-resistance and compatibility with polyimide, a matrix polymer. In this study, we designed oligo(amic acid) having alkyl side chains and terminal amine groups to satisfy previous requirements and studied the modification of surface of layered silicates. Oligo(amic acid)s were prepared by the reaction of diamine monomers and PMDA and their molecular weight was controlled in about 2000g/mol. After that, acidification and ion exchange reaction led to the high-temperature organophilic layered silicate (OLS). XRD pauerns of OLS showed the more increased gallery spacing by $4{\AA}$ than that of the pristine layered silicate and the initial decomposition temperatures of OLS were in above $280^{\circ}C$. The polyimide nanocomposite films based on heat resistant OLS showed that the OLSs were well dispersed through the matrix and their CTEs showed a decrease of $26\%$ compared with pristine polyimide films.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.94-104
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• 2019

In this study, we investigated the spatial variation in land use and topographic effects on water quality at the Geum river watershed in South Korea, using the ordinary least squares(OLS) and geographically weighted regression (GWR) models. Understanding the complex interactions between land use, slope, elevation, and water quality is essential for water pollution control and watershed management. We monitored four water quality indicators -total phosphorus, total nitrogen, biochemical oxygen demand, and dissolved oxygen levels - across three land use types (urban, agricultural, and forested) and two topographic features (elevation and mean slope). Results from GWR modeling revealed that land use and topography did not affect water quality consistently through space, but instead exhibited substantial spatial non-stationarity. The GWR model performed better than the OLS model as it produced a higher adjusted $R^2$ value. Spatial variation in interactions among variables could be visualized by mapping $R^2$ values from the GWR model at fine spatial resolution. Using the GWR model, we were able to identify local pollution sources, determine habitat status, and recommend appropriate land-use planning policies for watershed management.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.355-366
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• 2003

A unified procedure for principal component regression (PCR), partial least squares (PLS) and ordinary least squares (OLS) is proposed. The process gives solutions for PCR, PLS and OLS in a unified and non-iterative way. This enables us to see the interrelationships among the three regression coefficient vectors, and it is seen that the so-called E-matrix in the solution expression plays the key role in differentiating the methods. In addition to setting out the procedure, the paper also supplies a robust numerical algorithm for its implementation, which is used to show how the procedure performs on a real world data set.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.115-134
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• 1994

Polynomial errors-in-variables model with one predictor variable and one response variable is defined and an estimator of model is derived following the Booth's linear model estimation procedure. Since polynomial model is nonlinear function of the unknown regression coefficients and error-free predictors, it is nonlinear model in errors-in-variables model. As a result of applying linear model estimation method to nonlinear model, some additional assumptions are necessary. Hence, an estimator is derived under the assumption that the error variances are decrasing as sample size increases. Asymptotic propoerties of the derived estimator are provided. A simulation study is presented to compare the small sample properties of the derived estimator with those of OLS estimator.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.1
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• pp.163-170
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• 1996

An errors-in-variables model (EVM) differs from the classical regression model in that in the former the independent variable is also subject to error. This paper shows that to assess the applicability of the ordinary least squares (OLS) estimation procedure to the EVM, the relative dispersion of the independent variable to its error variance must be also considered in addition to Mandel's criterion. The effect of physically reducing the variance of errors in the independent variable on the performance of the OLS slope estimator is also discussed.

• Proceedings of KOSOMES biannual meeting
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• 2003.05a
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• pp.179-181
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• 2003

Spatio-temporal distributions of nighttime fishing fleets were described with the aid of geographic information system (GIS) technology in the East Sea, using daily mean composite images of the Defense Meteorological Satellite Program (DMSP) /Operational Linescan System (OLS) in 1993 and 1994. We selected a study area from 30$^{\circ}$N to 44$^{\circ}$N in latitude and from 124$^{\circ}$E to 142$^{\circ}$E in longitude in order to describe the monthly and seasonal changes of nighttime fishing fleets. The OLS images of nighttime visible band provide useful information about the spatio-temporal distribution of the fishing fleets. Density areas of nighttime fishing fleets were around Tsushima/Korea Strait. the east coast of the Korea Peninsula, the coast of Honshu, and around Yamato Bank.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.289-294
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• 2003

최근 활발히 행하여지는 금융 CRM(Customer Relationship Management)은 은행의수익성 제고를 위하여 개별고객의 주요특성(거래이력, 가치 등)을 파악하고, 이를 근거로 유사한 고객들을 분류하여 고객관리 방안을 찾는데 그 목적이 있다. 본 연구에서는 B은행의 실제 CRM을 통하여 수익성을 높일 수 있는 마케팅 시사점을 도출하고자 하며 이러한 마케팅의 도출과 목표가 되는 고객을 어떻게 선정할 것인가의 질문에 대한 방법으로 계량경제학 분야에서 기업단위연구의 생산효율성을 측정하기 위하여 사용되어지고 있는 SFM(Stochastic Frontier Model)과 OLS(Ordinary Linear Model)의 방법을 사용하였다.

• International Commerce and Information Review
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• v.11 no.2
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• pp.111-126
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• 2009

This study analysed determinants of Foreign Direct Investment to ASEAN+ 3 member nations using panel data for which cross-sectional data are combined with time series data. The data for the analysis included the amount of FDI, GDP, and indexes of economic independence. This study collected data from six nations(Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam) whose data were easily available, China and Japan from 2003 to 2007 and analysed them. The results are summarized as follows: Using the pooled OLS method, we found Model 2 had the highest explanatory power whose adjusted R-squared was 89.4%, which accounted for about 89% of foreign investment. Using the fixed effect model, Model 2 had the highest explanatory power whose adjusted R-squared was 96.8%, which accounted for about 97% of foreign investment. Using the probability effect model, Model 5 had the highest explanatory power, but in respect to its statistical significance, only GDP was 1% significant and the rest variables had no significance.

• International Journal of Human Ecology
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• v.13 no.1
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• pp.159-169
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• 2012

This study examined how financial education, impulsive buying, and socio-demographic factors affect saving behavior of 500 Korean college students. The descriptive results show that students who received financial education reported more positive saving behavior compared to students who did not receive financial education in school. The OLS results indicate that all else being equal, students with financial education reported more positive saving behavior than those without financial education. As predictors of saving behavior among Korean college students, the OLS results also reveal that impulsive buying, gender, and age were statistically significant. This study concludes that receiving financial education early, such as in elementary school, plays an important role in determining the saving behavior of Korean college students.