• ALGAE
• /
• 제17권2호
• /
• pp.105-115
• /
• 2002

Influences of vrious environmental factors on the eutrophication of Nakdong River were analyzed statistically using water samples collected from 1 January, 1999, to 30 September, 2001 at Namji area. The relationships between the concentration of chlorophyll α (eutrophication index) and environmental factors and were analyzed to develop a statistical model which can predict the status of eutrophication. The concentation of chlorophyll α ranged from 66.2 mg · $m^{-3}$ to 70.8 mg · $m^{-3}$ during dry winter season and the average concentration during this study period was 35.5 mg · $m^{-3}$ Namji area of Nakdong River was in the hypereutrohic stage in terms of water quality. Stephanodiscus sp. and Aulacoseria granulata var. angustissima were dominant species during the witnter to spring time and summer to autumn period, respectively. Based on the correlation analysis and the analysis of variance between chlorophyll α concentration and environmental factors, significantly high positive relationships were found in the order of BOD> pH> COD > KMnO₄ consumption > DO > conductivity > alkalinity. In contrast to these factors, significantly negrative relationships were found as in the order of $PO₄^{3-}-P$ >water level>the rate of Namgang-dam discharge > NH₃-N> the rate of Andong-dam discharge> the rate of Hapchoen-dam discharge. Based on the factors analysis of environmental factors on the concentration of chlorophyll α, we obtained five factors as follows. The first factor included water level, pH, turbiditiy, conductivity, alkalinity and the rate of Namgang-dam discharge. The second factor included water temperature DO, NH₄+-N, NO₃- -N. The third factor included KMnO₄ consumption COD and BOD. The fourth factor included the rate of Andong-dam discharge, the rate of Hapcheon-dam discharge, and the rate of Imha-dam discharge. The final factor included T-N T-P and $PO₄^{3-}-P$ > concentration. We derived two statistica models that can predict the occurrence of eutrophication based on the factors by factor analysis, using regression analysis. The first model is the stepwise regression model whose independent variables are the factors produced by factor analysis : chl α (mg · $m^{-3}$ = 42.923+(18.637 factor 3) + (-17.147 factor 1) + (-12.095 factor 5) + (-4.828 factor 4). The second model is the alternative stepwise regression model whose independent variables are the sums of the standardized main component variables:chl α (mg · $m^{-3}$ = 37.295+(7.326 Zfactor 3) + (-2.704 Zfactor 1)+(-2.341 Zfactor 5).

• 한국환경과학회지
• /
• 제24권6호
• /
• pp.743-753
• /
• 2015

In this study, it is an object to develop a regression model for the estimation of TOC (total organic carbon) concentration using investigated data for three years from 2010 to 2012 in the Gam Stream unit watershed, and applied in 2009 to verify the applicability of the regression model. TOC and $COD_{Mn}$ (chemical oxygen demand) were appeared to be derived the highest correlation. TOC was significantly correlated with 5 variables including BOD (biological oxygen demand), discharge, SS (suspended solids), Chl-a (chlorophyll a) and TP (total phosphorus) of p<0.01. As a result of PCA (principal component analysis) and FA (factor analysis), COD, TOC, SS, discharge, BOD and TP have been classified as a first factor. TOCe concentration was estimated using the model developed as an independent variable $BOD_5$ and $COD_{Mn}$. R squared value between TOC and measurement TOC is 0.745 and 0.822, respectively. The independent variable were added step by step while removing lower importance variable. Based on the developed optimal model, R squared value between measurement value and estimation value for TOC was 0.852. It was found that multiple independent variables might be a better the estimation of TOC concentration using the regression model equation(in a given sites).

• 대한간호학회지
• /
• 제49권5호
• /
• pp.575-585
• /
• 2019

Purpose: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. Methods: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. Results: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. Conclusion: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.

• 응용통계연구
• /
• 제21권4호
• /
• pp.603-613
• /
• 2008

셀 수 있는 이산 자료 중에서 일반적인 모형에 비하여 영의 빈도가 과도하게 많이 관측되는 자료가 있다. 이러한 경우에 포아송 또는 음이항회귀모형과 같은 일반적인 회귀모형에 의한 분석은 적절하지 못하다. 본 논문에서는 영과잉 포아송회귀모형과 영과잉 음이항회귀모형에 대하여 베이지안 분석을 하였다. 또한, 마코브 연쇄 몬테카롤로 방법으로 계산한 베이즈 요인을 이용하여 모형선택을 하였다. 실제 교통사고 자료를 분석하여 이론적인 결과들을 뒷받침하였다.

• 소성∙가공
• /
• 제25권3호
• /
• pp.182-188
• /
• 2016

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology conducive to producing multi-curvature surfaces by controlling the strain distribution along longitudinal direction. In FRRF, a sheet metal is shaped into the desired curvature by using reconfigurable rollers and gaps between the rollers. As FRRF technology and equipment are under development, a simulation model corresponding to the physical FRRF would aid in investigating how the shape of a sheet varies with input parameters. To facilitate the investigation, the current study exploits regression analysis to construct a predictive model for the longitudinal curvature of the sheet. Variables considered as input parameters are sheet compression ratio, radius of curvature in the transverse direction, and initial blank width. Samples were generated by a three-level, three-factor full factorial design, and both convex and saddle curvatures are represented by a quadratic regression model with two-factor interactions. The fitted quadratic equations were verified numerically with R-squared values and root mean square errors.

• 동아시아식생활학회지
• /
• 제17권6호
• /
• pp.963-971
• /
• 2007

This research aimed to determine the relationship between hotel restaurants' SCM activities and their results. The samples are included exclusive high-end hotels located in the seoul area. To analyze the data, frequency analysis, reliability analysis, factor analysis, and regression analysis were applied. Multiple regression analysis showed that SCM activities (${\beta}$=.342, p<.000), information sharing (${\beta}$=.136, p<.006), and cooperative activities (${\beta}$=.120, p<.015) had a significant impact on financial performance. The explanatory power of this model was 14%, and there was statistical significance in the regression model. SCM activities(${\beta}$=.221, p<.000), information sharing (${\beta}$=.475, p<.000), and cooperative activities (${\beta}$=.172, p<.000) also had a significant impact on non-financial performance, and the explanatory power of this model was 29%, with statistical significance in the regression model.

• Corrosion Science and Technology
• /
• 제19권6호
• /
• pp.302-309
• /
• 2020

This study aimed to predict the pitting corrosion characteristics of AL-6XN super-austenitic steel using multiple linear regression. The variables used in the model are degree of sensitization, temperature, and pH. Experiments were designed and cyclic polarization curve tests were conducted accordingly. The data obtained from the cyclic polarization curve tests were used as training data for the multiple linear regression model. The significance of each factor in the response (critical pitting potential, repassivation potential) was analyzed. The multiple linear regression model was validated using experimental conditions that were not included in the training data. As a result, the degree of sensitization showed a greater effect than the other variables. Multiple linear regression showed poor performance for prediction of repassivation potential. On the other hand, the model showed a considerable degree of predictive performance for critical pitting potential. The coefficient of determination (R2) was 0.7745. The possibility for pitting potential prediction was confirmed using multiple linear regression.

• International Journal of Computer Science & Network Security
• /
• 제23권9호
• /
• pp.37-46
• /
• 2023

The main aim of the article is to solve the problem of automating price monitoring using marketing forecasting methods and Excel functionality under martial law. The study used the method of algorithms, trend analysis, correlation and regression analysis, ANOVA, extrapolation, index method, etc. The importance of monitoring consumer price developments in market pricing at the macro and micro levels is proved. The introduction of a Dummy variable to account for the influence of martial law in market pricing is proposed, both in linear multiple regression modelling and in forecasting the components of the Consumer Price Index. Experimentally, the high reliability of forecasting based on a five-factor linear regression model with a Dummy variable was proved in comparison with a linear trend equation and a four-factor linear regression model. Pessimistic, realistic and optimistic scenarios were developed for forecasting the Consumer Price Index for the situation of the end of the Russian-Ukrainian war until the end of 2023 and separately until the end of 2024.

• 농촌의학ㆍ지역보건
• /
• 제41권4호
• /
• pp.183-194
• /
• 2016

Objectives: The purpose of this study was to investigate the differences of capacity of local health organization to regional characteristics and the influence of organizational capacity on organizational performance. Methods: The study used the secondary data for 160 local public health organizations from $5^{th}$ Community Health Plans and 2009 Community Health Survey. The collected data were analyzed using one-way ANOVA and multiple regression analysis. Results: Work force and budget showed differences in regional size and elderly population rate. And consumer satisfaction and health care utilization showed differenced in work force and budget. The regression model with total number of employee, number of registered nurses, number of doctors and budget against consumer satisfaction was statistically significant (F=14.70, p=<.001), and number of registered nurses was identified as a factor influencing consumer satisfaction. This model also explained 20.5% of service satisfaction. The regression model for consumer satisfaction was statistically significant (F=45.98, p=<.001), and total number of employee nurses was identified as a factor influencing health care utilization. This model also explained 53.1% of utilization. Conclusions: The findings of this study imply that organizational capacity as work force and budget should be increased to improve the organizational performance as consumer satisfaction and health care utilization.

• 스마트미디어저널
• /
• 제11권5호
• /
• pp.38-47
• /
• 2022

Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R² value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R² values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.