• 한국소음진동공학회논문집
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• 제20권2호
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• pp.172-178
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• 2010

The multiple regression analysis and path analysis in each dominant area of noise source are conducted to analyze the relationship between dependent variables like annoyance and independent ones such as noise and non-noise factors. The multiple regression analysis shows that impact of noise factors is the highest to annoyance in dominant areas of road traffic and railway noise. Meanwhile, impact of non-noise factors such as sensitivity and satisfaction of environment on annoyance is also high in these areas. The path analysis result for multivariate analysis between various independent and dependent variables is similar to that of the multiple regression analysis. However, noise factor is the greatest factor influent on annoyance in the dominant areas of the combined noise, and relationship between annoyance and sensitivity is the highest in combined area exposed to road traffic noise and railway noise.

• 한국IT서비스학회지
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• 제10권2호
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• pp.247-257
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• 2011

In this paper, we propose a multiple-disseminators determining mechanism for Efficient Code Dissemination with low-delay(ECoDi) for wireless sensor networks (WSN). Code dissemination is in the spotlight as an important research issue since sensor nodes are necessary for updating new software remotely or fixing bugs dynamically. In particular, the time factor for code dissemination is the most important factor in order that the normal operation of nodes can be continuously performed as soon as finishing the dissemination. For this factor, ECoDi determines the set of disseminators through regression analysis based on the size of distributed code and the time of past unicasts and broadcasts. Then it transmits the entire code as a unicast to multiple disseminators, and the disseminators broadcast the code to the remaining neighbor nodes. Performance results on a testbed show that ECoDi reduces dissemination time significantly compared to a conventional scheme.

• 한국환경과학회지
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• 제28권12호
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• pp.1101-1110
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• 2019

This study investigated Post-Occupancy Evaluation (POE) of cherry blossom trails 'Cherry Road' in Daegu Catholic Univ. campus, at Gyeonsan-city, Korea. The evaluation focused on image preference and satisfaction of users i.e., students, using questionnaire surveys. A total 201 questionnaire samples were analyzed and most of the respondents were in the age group of 20. Frequency analysis was conducted on demographics, use behavior, reliability, and means. Factor analysis and multiple regression analysis were conducted for user satisfaction and image preference. Over 80% of visitors came with companions during daytime. The most common motives for use were strolling and walking, event and meeting, passing. For user satisfaction the mean scores were highest for landscape beauty (4.22), image improvement (4.14), campus image (4.08). Night lighting facility received the lowest score (3.32). Factor analysis concerning user satisfaction was categorized into environment-human behavior and physical factors. Multiple regression analysis showed that the overall satisfaction of user was significantly influenced by five independent variables: 'harmonious' (β=.214), 'night lighting facility' (β=.173), 'landscape beauty' (β=.208), 'lawn care' (β=.154), and 'walking trails' (β=.123). The mean scores of image variables were highest for 'beautiful' (5.81), 'bright' (5.67), and 'open' (5.64). The lowest scores was for 'quiet' (4.47). Exploratory factor analysis led to three factors being categorized: aesthetics, comforts, and simplicity. Result of multiple regression analysis indicated that the preference of space image was significantly influenced by five variables: 'bright' (β=.397), 'refreshing' (β=.211), 'cool' (β=.219), 'clean' (β=.182), and 'natural' (β=.-142). Hence, Cherry Road has a high level of user satisfaction and image evaluation, which is interpreted as having various cultural events and value for students on campus. To improve the satisfaction of Cherry Road in the future, it is necessary to secure night lighting, to manage trash cans, and to secure rest space.

• 한국재료학회지
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• 제29권12호
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• pp.781-789
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• 2019

The prediction of Jominy hardness curves and the effect of alloying elements on the hardenability of boron steels (19 different steels) are investigated using multiple regression analysis. To evaluate the hardenability of boron steels, Jominy end quenching tests are performed. Regardless of the alloy type, lath martensite structure is observed at the quenching end, and ferrite and pearlite structures are detected in the core. Some bainite microstructure also appears in areas where hardness is sharply reduced. Through multiple regression analysis method, the average multiplying factor (regression coefficient) for each alloying element is derived. As a result, B is found to be 6308.6, C is 71.5, Si is 59.4, Mn is 25.5, Ti is 13.8, and Cr is 24.5. The valid concentration ranges of the main alloying elements are 19 ppm < B < 28 ppm, 0.17 < C < 0.27 wt%, 0.19 < Si < 0.30 wt%, 0.75 < Mn < 1.15 wt%, 0.15 < Cr < 0.82 wt%, and 3 < N < 7 ppm. It is possible to predict changes of hardenability and hardness curves based on the above method. In the validation results of the multiple regression analysis, it is confirmed that the measured hardness values are within the error range of the predicted curves, regardless of alloy type.

• 한국환경과학회지
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• 제16권11호
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.

• 한국항해항만학회지
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• 제30권7호
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• pp.579-583
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• 2006

The purpose of this paper is to study a relationship between alliance success factors and performances of strategic alliance. In order to achieve the purpose of this research, factor analysis, reliability and validity and regression method are used. In conclusion, alliance success factors can be divided as mutual complementarity and information sharing factor, sharing and mutual agreement of vision and goal factor, performance management factor and culture and organization factor. According to regression results, all of four factors affects significantly dependent variables. Among them, mutual complementarity and information sharing mostly affects each dependent variable.

• 한국환경과학회지
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• 제11권7호
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• pp.687-696
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• 2002

Air quality monitoring data and meteorology data which had collected from 1995. 1. to 1999. 2. in six areas of Daegu, Manchondong, Bokhyundong, Deamyungdong, Samdukdong, Leehyundong and Nowondong, were investigated to determine the distribution and characteristic of ozone. A equation of multiple regression was suggested after time series analysis of contribution factor and meteorology factor were investigated during the day which had high concentration of ozone. The results show the following; First, 63.6% of high ozone concentration days, more than 60 ppb of ozone concentration, were in May, June and September. The percentage of each area showed that; Manchondong 14.4%, Bokhyundong 15.4%, Deamyungdong 15.6%, Samdukdong 15.6%, Leehyundong 17.3% and Nowondong 21.6%. Second, correlation coefficients of ozone, $SO_2$, TSP, $NO_2$ and CO showed negative relationship; the results were respectively -0.229, -0.074, -0.387, -0.190(p<0.01), and humidity were -0.677. but temperature, amount of radiation and wind speed had positive relationship; the results were respectively 0.515, 0.509, 0.400(p<0.01). Third, $R^2$ of equation of multiple regression at each area showed that; Nowondong 45.4%, Lee hyundong 77.9%, Samdukdong 69.9%, Daemyungdong 78.8%, Manchondong 88.6%, Bokhyundong 77.6%. Including 1 hour prior ozone concentration, $R^2$ of each area was significantly increased; Nowondong 75.2%, Leehyundong 89.3%, Samdukdong 86.4%, Daemyungdong 88.6%, Manchondong 88.6%, Bokhyundong 88.0%. Using equation of multiple regression, There were some different $R^2$ between predicted value and observed value; Nowondong 48%, Leehyundong 77.5%, Samdukdong 58%, Daemyungdong 73.4%, Manchondong 77.7%, Bokhyundong 75.1%. $R^2$ of model including 1 hour prior ozone concentration was higher than equation of current day; Nowondong 82.5%, Leehyundong 88.3%, Samdukdong 80.7%, Daemyungdong 82.4%, Manchondong 87.6%, Bokhyundong 88.5%.

• Geomechanics and Engineering
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• 제13권2호
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2001년도 학술대회논문집
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• pp.199-202
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• 2001

This paper presents the correlation on the contamination degree and weather conditions using the multiple regression of the statistic method. The cotamination is a significant factor in the insulation system. The major factor is natural contamination, and the most is the salt. The salt contamination is occured by the weather conditions. The weather contaminations are wind, precipitation, temperature, relative humidity, etc. The effects of those are different, and the correlation is showed numerically by using the multiple regression method of the statistic method. With this, the estimation of the equivalent salt deposit density(ESDD) becomes possible by the changes of the weather conditions.

• Journal of Preventive Medicine and Public Health
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• 제46권1호
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• pp.28-38
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• 2013

Objectives: This study aimed to collect information that will help enhance the social networks and improve the quality of life among elderly people by observing the relationship between their social network and health-related quality of life (HRQoL) and by analyzing social network factors affecting HRQoL. Methods: This study was based on the 2008 Community Health Survey in Yeoncheon County. Three hundred elders were included in the study population. We compared the revised Lubben Social Network Scale (LSNS-R) score and Euro quality of life-5 dimensions health status index by demographic characteristics and chronic disease prevalence. We analyzed the data using multiple regression and tobit regression by setting the HRQoL as the dependent variable and social network and other characteristics as the independent variables. We analyzed social network factors by using factor analysis. Results: The LSNS-R score differed significantly according to age and existence of a spouse. According to the results from the hierarchical multiple regression analysis, the LSNS-R explained 0.10 of the variance and LSNS-R friends factor explained 0.10 of the variance. The tobit regression indicated that the contribution of the LSNS-R family size factor to the regression coefficient of the independent variable that affected the HRQoL was $B_T$=2.96, that of the LSNS-R family frequency factor was $B_T$=3.60, and that of LSNS-R friends factor was $B_T$=5.41. Conclusions: Social networks among elderly people had a significant effect on HRQoL and their networks of friends had a relatively higher effect than those of family members.