• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2002.02a
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• pp.133-138
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• 2002

온풍난방기 20대를 대상으로 온풍난방기의 베기가스중 탄산가스농도, 온풍온도차, 배기가스온도 및 열효율을 조사하여 사용연수별로 분석하였으며 주요 결과는 다음과 같다. 가. 사용연수별 온풍난방기 배기가스중 탄산가스농도와 온풍온도차는 사용연수에 따른 조사표본의 부족으로 큰 차이가 있었다고 하기는 어렵고 정확한 조사를 위해서는 보다 많은 대수가 필요할 것으로 판단된다. 나. 사용연수별 배기가스온도는 사용연한이 오래될수록 높아졌다고 판단된다. 이는 열교환기에서 흡입공기와 연소열이 열교환이 충분히 이루어지지 않았거나, 버너노즐의 노후화, 버너송풍기, 온풍난방기 송풍기의 노후화에 따른 결과라 여겨진다. 사용연수에 따른 배기가스온도는 회귀방정식 Y= 79.032Ln(X) + 116.66 ($R^2$= 0.6784)로 나타낼 수 있었다. 다. 사용연수별 온풍난방기의 열효율은 사용연한이 오래될수록 감소하는 경향을 보였으며 회귀방정식 Y = 95.167 X $^{-0.054}$ ($R^2$= 0.5696)로 나타낼 수 있었다.

• Survey Research
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• v.2 no.1
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• pp.73-86
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• 2001

In this paper. we conducted an empirical study to investigate the design and weighting effects on descriptive and analytic statistics. We compared the regression models using the design-based approach and the generalized estimating equations (GEEs) approach with the model-based approach through the design and weighting effects analysis.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.89-98
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• 2017

This study analyzed empirically the same data through SPSS statistics(regression analysis) and AMOS program(structural equation model) used for cause and effect analysis. The result of empirical analysis of moderation effect was as follows. Meanwhile, SPSS statistics(regression analysis) did not pictured moderation effect in the categorical data(sex) and continous data(satisfaction of consunting), AMOS program(structural equation model) pictured partial moderation effect about the effecting of consultant's capability and attitude on the consulting repurchase within 10% level of significant. Eventually, This study showed that AMOS program and SPSS statistics used different methology in moderation effect, thus the different outcomes appeared although using the same data.

• Journal of Korea Water Resources Association
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• v.39 no.5 s.166
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• pp.395-403
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• 2006

This study suggests the use of a simple method, called the unit concentration response function(UCRF) for predicting travel time and dispersion of pollutants with the minimum information of study area instead of numerical models which are widely used In the Previous studies. However, the numerical models require time-consuming, tedious effort, and many data sets. So we derive the UCRF using some components such as travel time, peak concentration, and passage time of pollutant etc. We use the regression equation for the estimations of components which were developed from the investigations of many river basins in USA. This study used the regression equaiton for the UCRF to the accident of Dichloromethane leak into the Nakdong River occurred on June 30, 1994 and applied the UCRF for the predictions of travel time and dispersion. The predictions were compared with the results by QUAL2E model. The results by the regression equaiton and QUAL2E model had a good agreement between observed and simulated concentrations. Therefore, the regression equation for the UCRF which can simply estimate travel time and concentration of pollutants showed its applicability for the ungaged basin.

• Journal of Embryo Transfer
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• v.18 no.3
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• pp.215-225
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• 2003

This study was carried out to investigate the change of blood compositions by age in Hanwoo, and a total of 866 of Hanwoo, which consisted with 638 of steer and 228 of bulls, were used to measure serum concentrations. A multiple regression equation was estimated with collection age and blood composition as independent and dependent variables, respectively. Complicated regression equations for blood compositions in steer and bulls were IGF-I(cubic), calcium (linear), and IP(linear). Linear and cubic equations were fitted to testosterone in steer and creatinine in bulls, respectively. A cubic equation in steer and linear equation in bulls were fitted to HDLC. Equations of quadratic in steer and cubic in bulls were fitted to concentration of triglyceride, globulin, and A/G ratio. BUN was fitted by equations of cubic in steer and quadratic in bulls. TP and albumin were fitted by equations of quadratic in steer and linear in bulls. A cubic regression equation did not explain the change of cortisol by age in steer and bulls. A cubic regression equation did explain the change of glucose by age in steer, but not in bulls. Higher R-square values (R-SQUARE>0.1) were estimated to IGF-1, albumin, creatinine, Inorganic phosphorous(IP) and HDLC in steer, and testosterone, IGF-I, TP, albumin, glucose, creatinine, IP, and HDLC in bulls for the fitted regression equations of blood compositions. Therefore, IGF-I, albumin, creatinine, IP, and HDLC were regarded as comparatively large variation by age in steer and bulls.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.65-81
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• 1998

Simultaneous equation models, increasingly used in many detailed analyses, tend to get larger and more sophisticated to describe the structure of the study area to be close to the actual situations. In setting up such a system of equations, statistical results and simulation performance of the model as a whole may be meaningless and unrepresentative of the real world due to a structural instability that is built into the model when the equations are combined and solved simultaneously. Even though the use and subsequent analysis of an unstable system are likely to mislead us, most of the studies that take the simultaneous equation approaches neglect such a serious problem. Thus it is necessary to illustrate how to check the stability problem and apply to the actual model, then investigate how such as analysis is able to provide useful information about the structural characteristics of the model from the dynamic viewpoint.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.207-216
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• 2011

Bootstrap is a resampling technique to find an estimate of parameters or to evaluate the estimate. This technique has been used in estimating parameters in linear model(LM) and generalized linear model(GLM). In this paper, we explore the possibility of applying Bootstrapping Residuals, Pairs, and an Estimating Equation that are most widely used in LM and GLM to the generalized estimating equation(GEE) algorithm for modelling repeatedly measured regression data sets. We compared three bootstrapping methods with coefficient and standard error estimates of GEE models from one simulated and one real data set. Overall, the estimates obtained from bootstrap methods are quite comparable, except that estimates from bootstrapping pairs are somewhat different from others. We conjecture that the strange behavior of estimates from bootstrapping pairs comes from the inconsistency of those estimates. However, we need a more thorough simulation study to generalize it since those results are coming from only two small data sets.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.697-712
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• 2013

When analyzing repeated binary data, the generalized estimating equations(GEE) approach produces consistent estimates for regression parameters even if an incorrect working correlation matrix is used. However, time-varying covariates experience larger changes in coefficients than time-invariant covariates across various working correlation structures for finite samples. In addition, the GEE approach may give biased estimates under missing at random(MAR). Weighted estimating equations and multiple imputation methods have been proposed to reduce biases in parameter estimates under MAR. This article studies if the two methods produce robust estimates across various working correlation structures for longitudinal binary data with time-varying covariates under different missing mechanisms. Through simulation, we observe that time-varying covariates have greater differences in parameter estimates across different working correlation structures than time-invariant covariates. The multiple imputation method produces more robust estimates under any working correlation structure and smaller biases compared to the other two methods.

• Journal of the Korean Statistical Society
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• v.4 no.1
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• pp.57-66
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• 1975

본논문은 단일방정식 추정에 있어서 사전적으로 주어진 모수에 대한 제약을 추정전의 정보로써 피설명변수(explained variable)에 적용하여 회귀하였을 때 추정량이 어떻게 바뀌게 되는가 하는 것을 고찰하고 이를 발전시켜서 반복적인 계산방법을 적용하는 특정의 추정기법에 대하여 시론적으로 접근하는 데에 목적이 있다.