• Journal of The Korean Society of Agricultural Engineers
• /
• v.54 no.3
• /
• pp.141-148
• /
• 2012

Food policy is considered as the most basic and central issue for all countries, while making efforts to keep each country's food sovereignty and enhance food self-sufficiency. In the case of Korea where the staple food is rice, the rice yield prediction is regarded as a very important task to cope with unstable food supply at a national level. In this study, Korean paddy Rice yield Prediction Model (KRPM) developed to predict the paddy rice yield using meteorological element and MODIS NDVI. A multiple linear regression analysis was carried out by using the NDVI extracted from satellite image. Six meteorological elements include average temperature; maximum temperature; minimum temperature; rainfall; accumulated rainfall and duration of sunshine. Concerning the evaluation for the applicability of the KRPM, the accuracy assessment was carried out through correlation analysis between predicted and provided data by the National Statistical Office of paddy rice yield in 2011. The 2011 predicted yield of paddy rice by KRPM was 505 kg/10a at whole country level and 487 kg/10a by agroclimatic zones using stepwise regression while the predicted value by KOrea Statistical Information Service was 532 kg/10a. The characteristics of changes in paddy rice yield according to NDVI and other meteorological elements were well reflected by the KRPM.

• Journal of the Korean Society of Safety
• /
• v.37 no.6
• /
• pp.128-135
• /
• 2022

The comparative evaluation of occupational accident fatality rates (OAFRs) of different countries is complicated owing to the differences in their level of socio-economic development. However, such evaluation is necessary to assess the national occupational safety and health system of a country. This study proposes a statistical method to compare the OAFRs of countries taking into consideration the difference in their level of socio-economic development. We first collected data on the socio-economic indicators and OAFRs of 11 countries over a 30-year period. Next, based on literature survey and statistical correlation analysis, we selected the significant independent variables and built multiple linear regression models to predict OAFR. We also determined the groups of countries having heterogeneous relationships between the independent variables and OAFRs, which are represented by the regression models. The proposed method is demonstrated by comparing the OAFR of Korea with the OAFRs of 10 other developed countries.

• Journal of dental hygiene science
• /
• v.19 no.4
• /
• pp.271-278
• /
• 2019

Background: This study aimed to examine the factors that influence clinical performance of dental hygiene students to provide useful data for developing strategies to improve clinical competence. Methods: The effects of variables on clinical competence by quantile level were analyzed using quantile regression analysis in 247 dental hygiene students. Quantile regression and multiple regression analyses were conducted using the Stata 11.0 program to analyze predictors of clinical competence. Results: The clinical competence score of dental hygiene students was 42.69±5.90, the satisfaction of clinical practice was 49.90±7.44, the clinical practice stress was 50.62±7.37, and the professional self-concept was 31.68±4.41. Empathy was the highest at 50.87±4.93. Multiple regression analysis showed that school year, stress from clinical training, satisfaction with clinical training, professional self-concept, and empathy had significant impact on clinical competence. Quantile regression analysis showed that the effects varied depending on the clinical competence level. School year and professional self-concept had a significant positive effect, regardless of the clinical competence level, while empathy had a significant positive effect at the top 10% (Q90) of the clinical competence level. Satisfaction with clinical practice affected clinical competence at Q25, Q50, and Q90. Stress from clinical practice had significant effects at Q25, Q50, and Q90 (p<0.05). Conclusion: According to the study results, different factors affected clinical competence according to the quantile of clinical competence. This study provides valuable implications for designing clinical competence enhancement programs and strategies. In addition, objective indicators for considering factors that may affect the clinical competence, such as academic competence and satisfaction of practice hospitals, are expected to require detailed analysis and measures.

• Journal of Environmental Health Sciences
• /
• v.7 no.1
• /
• pp.9-20
• /
• 1981

This study was performed to determine the correlationship between temperature and overall removals of BOD, SS and to demonstrate the effect of temperature on treatment performance. These data for a period from February 1, 1977 to January 31, 1980 were obtained from the Cheong-Gye Cheon Sewage Treatment plant. The results of correlation and stepwise multiple regression analysis were as follows. 1) Secondary effluent BOD and SS showed negative correlationship with water temperature, with correlation coefficient of -0.1710, and -0.1654 respectively. 2) Correlation coefficient of BOD, SS removal rate and water temperature were 0.1823 and 0.0429 respectively. 3) Regresion equation for estimate of BOD removal rate was as follows $\widehat{Y}_1$ (BOD removal rate)=63.9994+0.5442X(water temperature). And BOD removal rate showed non significant change according to the water temperature. 4) Regression equation for estimate of SS removal rate was as follows $\widehat{Y}_2$ (SS removal rate)=61.6881+0.1514X(Water temperature). And SS removal rate showed non significant change according to the water temperature. 5) According to the Stepwise Multiple Regression analysis, water temperature ranked second order in the BOD removal rate estimation and the equation was as follows $\widehat{Y}_1$ (BOD removal rate)=69.7398+0.2665 $X_1$ (Primary effluent BOD)+0.3562 $X_2$ (Water temperature)-0.0122 $X_3(Flow)+4413.271X_4$ (Organic Loading).

• Journal of Aerospace System Engineering
• /
• v.10 no.3
• /
• pp.9-14
• /
• 2016

Using a multiple regression analysis, a total of 78 low-Reynolds-number airfoils are examined in this paper to clarify the systematic relationships between the geometrical parameters of the airfoils and experimentally-determined aerodynamic coefficients. The results show that the effects of the maximum camber and the maximum thickness regarding the maximum lift and the stalling angle of attack, respectively, are major. The lower-surface flatness of the airfoil is also a crucial geometrical parameter for aerodynamic performance. It is proven here that, generally, the application of the regression equations for an assessment of the aerodynamic performance is relatively acceptable, along with an expectation that the lift-curve slope violates the normality assumption.

• Journal of Distribution Science
• /
• v.12 no.3
• /
• pp.17-24
• /
• 2014

Purpose - This study investigated the main variables of consumer food choice motive and how they affect attitude and purchase intention toward organic foods. The study involved a multiple regression analysis to verify the influence of food choice motive on attitude toward organic food. Research Design, Data, and Methodology - Data was collected through surveys of 280 students and ordinary citizens in Seoul and the Gyeonggi region, using sampling. A multiple regression analysis was performed to confirm the impact of food choice motive on attitude toward organic food, and a regression analysis was performed to identify the impact of attitude toward organic food on purchase intention. Results - Health and environment, among food choice motives, had significant positive influence on attitude toward organic food, whereas convenience, price, and familiarity had no impact. Attitude toward organic food had significant positive influence on organic food purchase intention. Conclusions - As this study identified the impact of organic food choice motive, it may provide baseline data for marketing strategies, to understand consumer attitude toward organic food and purchase intention, and to satisfy consumer needs.

• Journal of Korean Society for Quality Management
• /
• v.21 no.1
• /
• pp.161-170
• /
• 1993

This study attempts to present the linear regression analysis that involves more than one regressor variable, because regression analysis is the most widely used statistical technique for describing, predicting and estimating the relationships between given data. The model of multiple linear regression may be solved directly by the two linear programming methods, i.e., to minimize the sum of the absolute deviation (MSD) and to minimize the maximum deviation(MMD). In addition, some results was compared to each techniques for accuracy and tested to the validity of statistical meaning.

• The Journal of Bigdata
• /
• v.1 no.1
• /
• pp.9-17
• /
• 2016

This study identifies major features in water supply and introduces important factors in water services based on the information from data mining analysis of water quantity and water pressure measured from sensors. It also suggests more accurate methods using multiple regression analysis and neural network in predicting short term prediction of water demand in water service. A small block of a county is selected for the data collection and tests. There isa water demand on business such as public offices and hospitalstoo in this area. Real stream data from sensors in this area is collected. Among 2,728 data sets collected, 2,632 sets are used for modelling and 96 sets are used for testing. The shows that neural network is better than multiple regression analysis in their prediction performance.

• International Journal of Automotive Technology
• /
• v.4 no.2
• /
• pp.101-108
• /
• 2003

There are a lot of factors that influence automotive fuel economy such as average trip time per kilometer, average trip speed, the number of times of vehicle stationary, and so forth. These factors depend on road conditions and traffic environment. In this study, various driving data were measured and recorded during road tests in Seoul. The accumulated road test mileage is around 1,300 kilometers. The objective of the study is to identify the driving patterns of the Seoul metropolitan area and to analyze the fuel economy based on these driving patterns. The driving data which was acquired through road tests was analysed statistically in order to obtain the driving characteristics via modal analysis, speed analysis, and speed-acceleration analysis. Moreover, the driving data was analyzed by multivariate statistical techniques including correlation analysis, principal component analysis, and multiple linear regression analysis in order to obtain the relationships between influencing factors on fuel economy. The analyzed results show that the average speed is around 29.2 km/h, and the average fuel economy is 10.23 km/L. The vehicle speed of the Seoul metropolitan area is slower, and the stop-and-go operation is more frequent than FTP-75 test mode which is used for emission and fuel economy tests. The average trip time per kilometer is one of the most important factors in fuel consumption, and the increase of the average speed is desirable for reducing emissions and fuel consumption.

• Journal of Acupuncture Research
• /
• v.35 no.4
• /
• pp.226-237
• /
• 2018

Background: This study examined the characteristics and prognosis of patients admitted to the Dunsan Korean medicine hospital for treatment of herniated intervertebral disc (HIVD) or spinal stenosis with Miniscalpel needle therapy (MSN). Methods: Patients were admitted to the Dunsan Korean medicine hospital from January 01, 2016 to September 30, 2017 for the treatment of HIVD or spinal stenosis with MSN. Crossover analysis, Independent sample t test, one-way ANOVA, multiple linear regression analysis, and binary logistic regression analysis were performed. Results: Crossover analysis showed statistically significant differences in treatment methods according to gender, current pain according to the disease duration, satisfaction of MSN according to disease duration, treatment methods, and intention of re-treatment with MSN according to treatment methods. Independent t test and one-way ANOVA showed that there was a difference in current Numeric Rating Scale (NRS) according to disease duration, and difference between discharge and current NRS, and number of MSN according to disease. Multiple linear regression analysis showed that age, disease duration, and number of MSN affect discharge NRS, disease duration, and number of MSN affect current NRS, and Western medical treatment after MSN, discharge NRS, and current NRS affect satisfaction of MSN. Binary logistic regression analysis showed that discharge NRS affects current pain, and gender, discharge NRS, and treatment methods affect intention of re-treatment with MSN. Conclusion: Characteristics, prognosis, satisfaction and variables affecting prognosis of MSN were statistically significant, indicating that more systematic studies are required to further examine the effects of MSN on HIVD or spinal stenosis.