• Magazine of the Korean Society of Agricultural Engineers
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• v.22 no.3
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.633-641
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• 2000

In this paper, we propose a method to identify multiple outliers in regression analysis with only assumption of smoothness on the regression function. Our method uses single-linkage clustering algorithm and Projection Pursuit Regression (PPR). It was compared with existing methods using several simulated and real examples and turned out to be very useful in regression problem with the regression function which is far from linear.

• Journal of Families and Better Life
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• v.17 no.3
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• pp.131-144
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• 1999

The purpose of this study was to examine the influences of the consumer's knowledge the consumer's attitude the family characteristics and the variables on consumer socialization to the consumer's functions of homemakers. The samples were selected from 428 homemakers living in Kwangju, Frequncies Perentiles Means Standard Deviations Multiple regression Path analysis were used as statistical analysis The results were sumarized as follows: Resulting from multiple regression analysis the consumer's function had the positive linear relationships with variables such as family life cycles interaction with family consume knowledge and consumer attitude. The most influential variable was consumer attitude.

• Child Health Nursing Research
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• v.16 no.4
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• pp.297-303
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• 2010

Purpose: The purpose of this study was to identify the relationship of self-differentiation, family function and mental health among adolescents. Methods: The data were collected from 967 adolescents and analyzed using descriptive statistics, t-test, ANOVA, Scheffe's test, Pearson correlation coefficient and Stepwise multiple regression with the SPSS program. Results: Mental health differed according to grades, sibling position, father's education and mother's education. Self-differentiation and family function had a significant negative correlation with mental health. Multiple regression analysis showed recognition.emotional function, emotional cutoff and family projection as influencing self-differentiation. Grades, affective responsiveness in family function, and sibling position explained 20.8% of the total variance in mental health. Conclusion: The findings show that self-differentiation and family function influence mental health, indicating a need to develop nursing intervention programs to enhance adolescents' mental health and prevent negative outcomes. For these programs, the family must be included.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.319-329
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• 2014

Purpose: The purpose of this study was to investigate the effect of adolescent's family function, impulsive behavior, stress on the bullying types. Method: Data were collected from 627 adolescents and analyzed using descriptive statistics, t-test, Pearson correlation coefficients and stepwise multiple regression with the SPSS 18.0. Results: The bullying types (injurer and victim) correlates with family function, impulsive behavior and stress. Stepwise multiple regression analysis showed emotional reactivity, non-planning impulsiveness, friends related stress, experience of drinking (yes), experience of parent depression problem (yes), explained 34.1% of the total variance in bully injurer. Stepwise multiple regression analysis showed communication, motor impulsiveness, friends related stress, gender (male), grade (junior high school), explained 30.9% of the total variance in bully victim. Conclusion: The results of this study are expected to be used as basic data in providing a better understanding of adolescents' bullying, in preventing bullying and in developing an intervention program.

• Journal of the Korean Statistical Society
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• v.31 no.4
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• pp.509-518
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• 2002

This paper is concerned with semiparametric Bayesian analysis of the proportional intensity regression model of the Poisson process for multiple event time data. A nonparametric prior distribution is put on the baseline cumulative intensity function and a usual parametric prior distribution is given to the regression parameter. Also we allow heterogeneity among the intensity processes in different subjects by using unobserved random frailty components. Gibbs sampling approach with the Metropolis-Hastings algorithm is used to explore the posterior distributions. Finally, the results are applied to a real data set.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.507-513
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• 2004

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Asian Oncology Nursing
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• v.12 no.2
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• pp.156-165
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• 2012

Purpose: This study was to identify predictors of sexual function in gynecologic cancer patients. Methods: The participants were 154 patients treated at a university medical center in A city, Korea. The data collection was performed through a structured questionnaire from July to December, 2010. The instruments used in this study were Female Sexual Function Index (FSFI) perceived health status scale, Eastern Cooperative Oncology Group (ECOG) performance status, body image, and depression. Data were analyzed using descriptive statistics, Mann-Whitney test, Kruskal-Wallis test and stepwise multiple regression with the SPSS 18.0. Results: The mean score of perceived health status was 8.42 and sexual function was 8.42. The lowest score among sexual function was lubrication. The scores of sexual function was significantly different by age, job, marital status, period after diagnosis of cancer and diagnosis. There were significant correlations between sexual function, perceived health status, ECOG performance, body image and depression. In multiple regression analysis, predictors were identified as ECOG performance, age, diagnosis and period after diagnosis of cancer (Adj.$R^2$=.28). The most powerful predictor of female sexual function was ECOG performance (19.0%). Conclusion: The above findings indicate that it is necessary to develop a more effective and personalized sexual function improvement program for gynecologic cancer patient.

• Fire Science and Engineering
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• v.28 no.2
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• pp.69-75
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• 2014

This study derives the factors which affect the occurrence of arson from statistical data (population, economic, and social factors) by multiple regression analysis. Multiple regression analysis applies to 4 forms of functions, linear functions, semi-log functions, inverse log functions, and dual log functions. Also analysis respectively functions by using the stepwise progress which considered selection and deletion of the independent variable factors by each steps. In order to solve a problem of multiple regression analysis, autocorrelation and multicollinearity, Variance Inflation Factor (VIF) and the Durbin-Watson coefficient were considered. Through the analysis, the optimal model was determined by adjusted Rsquared which means statistical significance used determination, Adjusted R-squared of linear function is scored 0.935 (93.5%), the highest of the 4 forms of function, and so linear function is the optimal model in this study. Then interpretation to the optimal model is conducted. As a result of the analysis, the factors affecting the arson were resulted in lines, the incidence of crime (0.829), the general divorce rate (0.151), the financial autonomy rate (0.149), and the consumer price index (0.099).

• KIEAE Journal
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• v.9 no.1
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• pp.107-113
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• 2009

The energy cost is resulted from the energy use. Its sources are divided into some types and depended on the building use or energy-use type. The energy cost should be affected by the amount of the energy use. The cost could be calculated to consider various factors such as the insulation, heating type, building shape and others. But it can not consider all of the affect factors to the energy cost and need to categorize the factors to the condition for estimating the cost. In this paper, it aimed at providing the estimation model in linear equation and multiple linear regression, utilizing the building exterior condition and management characteristics in apartment housing. Its survey are conducted in two parts of management characteristics and building exterior condition. The correlation analysis is conducted to get rid of the multicolinearity among the inputted factors. The number of linear equation model is 11 and includes the 1st, 2nd and 3rd equation function, power function and others. Among these, it suggested the 2nd and 3rd function and power function in terms of the statistics. In multiple linear regression model, the building volume and management area are inputted to the estimation.