• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.193-209
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• 2006

Multiple regression is used to examine the relationship between a set of two or more independent variables and one dependent variable. It provides the information necessary to make predictions of the dependent variables based on several independent variables. To do so, the multiple regression equation is extended to: y=$a+{\beta}_1x_1+{\beta}_2x_2+{\ldots}+{\beta}_kx_k$ y=attractiveness a=the value of the intercept ${\beta}_1$=the slope(weighting) of the first variable ${\beta}_1$= the slope(weighting) of the second variable ${\beta}_k$=the slope of the $\kappa$th variable The resulting regression equation of this research is y=$a+{\beta}_1site's\;system+{\beta}_2customer\;satisfaction+{\beta}_3products+{\beta}_4delivery$ y=3.233+0.374(site's system)+0.268(customer satisfaction)+0.17(products)+0.077(delivery)

• Journal of Korean Biological Nursing Science
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• v.19 no.4
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• pp.241-251
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• 2017

Purpose: This study aimed to examine the factors related to the bone mineral density (BMD) in male and female adults aged 50 years or older. The factors included were socio-demographics, body mass index (BMI), blood pressure, blood lipids, and health habits. Methods: As a secondary data analysis, this study used raw data from the 5th Korean National Health and Nutrition Examination Survey, and selected 2,295 adults aged over 50 years who participated in the survey and had BMD values. The T-score of the femoral neck was evaluated for BMD. Data were analyzed using descriptive statistics, ANOVA and multiple regression. Results: The factors influencing the BMD in men were age (${\beta}=-.029$, p<.001), BMI (${\beta}=.100$, p<.001), triglyceride (${\beta}=-.001$, p=.001), and calcium (${\beta}=.001$, p=.003), and the suggested regression equation explained 25.6% of the variance in BMD (F=16.32, p<.001). The factors related to BMD in women were age (${\beta}=-.060$, p<.001), BMI (${\beta}=.091$, p<.001), total cholesterol (${\beta}=-.002$, p=.028), HDL cholesterol (${\beta}=.009$, p=.001), and calcium (${\beta}=.001$, p=.004), and the suggested regression equation explained 47.0% of the variance in BMD (F=61.72, p<.001). Conclusion: Based on the findings of this study, the modifiable factors, such as BMI, blood cholesterol, and calcium, should be targeted to improve the BMD in both men and women.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.173-181
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• 2019

The purpose of this study was to identify the effects of gender role conflicts, professional nursing value on career preparation behaviors of male nursing university student. Data were collected from 127 male nursing university student in grade 2-4 by using structured questionnaires from July 1 to November 30, 2018. The collected data were analyzed by descriptive statistics, independent t-test, ANOVA, Pearson's correlation coefficients and stepwise multiple regression by using SPSS Win 24.0 program. Multiple regression analysis showed that the predictors of career preparation behaviors were professional nursing values(${\beta}=0.28$, p=.001), satisfaction with major (${\beta}=0.23$, p=.006), club(${\beta}=.19$, p=.020) and the regression equation explained 20.8% of career preparation behaviors. Based on the results of this study, it is necessary to provide career educational programs considering according to the academic year of male nursing students and to develop educational programs to improve the nursing professionalism of male nursing students.

• Clinics in Shoulder and Elbow
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• v.16 no.1
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• pp.63-72
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• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.28-38
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• 2019

The purpose of this study was to identify factors influencing nursing performance of nurses in general hospitals. The participants were 251 nurses working in general hospitals in B city. SPSS/WIN 23.0 was used for analysis with descriptive statistics, t-test, ANOVA, $Scheff{\acute{e}}$ test, Pearson correlation coefficients and multiple regression. The result of the multiple regression indicates self-leadership(${\beta}=.43$, p<.001), total clinical career(${\beta}=.26$, p<.001), job satisfaction(${\beta}=.14$, p=.010), and annual self-development activity count(${\beta}=.10$, p=.049) were associated with nursing performance. These factors accounted for 38.5% of the total variance in nursing performance. Considering the results of this study, multi-dimensional programs to improve self-leadership and job satisfaction should be developed and implemented in order to foster nursing performance in clinical practice.

• The Journal of Korean Society for School & Community Health Education
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• v.13 no.3
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• pp.43-56
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• 2012

Objectives: The main purpose of this study was to examine factors influencing depression of middle school students in relation to coping behavior, social support, and self-esteem. Methods: The questionnaire survey was carried out on a convenience sample of 442 middle school students. The data analysis procedure included frequency, t-test, ANOVA, Pearson correlation coefficients, and stepwise multiple regression using depression as the dependent variable. Results: The prevalence of depression was 16.7%. Multiple regression revealed that the factors such as "negative" coping(${\beta}$=.29, p<.001), friend support(${\beta}$=-.23, p<.001), "social support pursuing" coping(${\beta}$=-.22, p<.001), school record(${\beta}$=.15, p<.001), self-esteem(${\beta}$=-.13, p<.01), family support(${\beta}$=-.13, p<.01), "agressive" coping(${\beta}$=.11, p<.01) turned out to be significant affecting factors. Forty six percent of varience in depression was explained by these factors. Conclusion: The results of this study suggest that to prevent the occurrence of depression, psychosocial aspects including emotional support should be considered when developing mental health education program for middle school students.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.212-228
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• 2004

This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99％ confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.

• The Journal of Korean Academic Society of Nursing Education
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• v.21 no.2
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• pp.276-284
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• 2015

Purpose: This study was conducted to investigate the factors that affect subjective well-being in nursing students. Methods: A total of 236 nursing students participated in the study. Data was collected from November through December 2013 using a self-reporting questionnaire that surveyed the level of gratitude, interpersonal relationship ability, subjective happiness, and life satisfaction. Data was analyzed by descriptive analysis, t-test, ANOVA, Pearson correlation coefficients and multiple regression with the SPSS/WIN 18.0 program. Results: Subjective happiness positively correlated with gratitude (r=.41, p<.001) and interpersonal relationship ability (r=.55, p<.001). Life satisfaction positively correlated with gratitude (r=.53, p<.001) and interpersonal relationship ability (r=.37, p<.001). Significant predictors of subjective happiness included gratitude (${\beta}=.38$, p<.001), interpersonal relationship ability (${\beta}=.28$, p<.001) and major satisfaction (${\beta}=.17$, p=.002). The regression model explained 39.3% of subjective happiness. Significant predictors of life satisfaction included gratitude (${\beta}=.42$, p<.001), academic achievement (${\beta}=.15$, p=.006), interpersonal relationship ability (${\beta}=.16$, p=.008) and major satisfaction (${\beta}=.12$, p=.035). The regression model explained 35.3% of life satisfaction. Conclusion: To improve subjective well-being among nursing students, nurse educators should develop educational intervention programs to promote gratitude and interpersonal relationships.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.418-418
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• 2012

현재 전 세계적으로 온실가스 농도 증가로 호우나 가뭄, 대설 등 지역에 따라 서로 상반되는 변화를 가져올 수 있다고 경고되고 있으며, 우리나라에서도 남해안지역과 경기북부지역에서 호우빈도가 증가하는 반면, 충정도 내륙지역과 경상북도에서는 호우빈도가 감소하고 5일 누적 강수량 또한 감소하여, 해당지역에서 가뭄이 발생할 경우 심화될 가능성이 높아진다고 보고된 바 있다. 기후변화 시나리오에 분석결과에서도 우리나라의 경우 평균적으로 강우일수는 작아지며, 강우강도는 커지는 결과들이 도출되었다. 이러한 결과들은 가뭄의 발생가능성이 높아지고 있음을 보여주고 있다. 본 연구에서는 우리나라에서 발생된 가뭄의 특성을 분석하고 가뭄의 특성과 기상인자간의 관계를 Quantile regression 분석을 통해 살펴보고자 한다. 가뭄의 특성과 기상인자(엘니뇨, 강수량 등)의 관계에 있어서 기상인자들의 평균을 이용하는 일반적인 회귀분석은 전체 데이터의 영향에 따른 가뭄특성인자와의 관계를 보여준다. 하지만 강수량과 가뭄과의 관계에서와 같이 강수량의 극값보다는 적은 강수량 혹은 무강우일수가 가뭄과 밀접한 관련을 보여준다. 이러한 점에서 이상치들에 영향을 배재할 수 있는 Quantile regression을 사용하여 Quantile에 따른 기상인자와 가뭄특성과의 관계를 규명하고 평가해 보고자 한다. 본 연구에서 적용한 Quantile Regression 기법은 회귀계수의 추정에 있어서 회귀인자의 신뢰성을 아래와 같은 Quantile-회귀계수 그래프를 통해 분석할 수 있으며, 로버스트 통계량의 특징인 분산이 적은 안정적인 추정량을 확보할 수 있는 장점을 갖는다. 아래식은 Quantile regression의 회귀계수 추정식을 나타낸다. $$arg\;in\;{n\\\;p(y_i-f(x_i,\;z_i,\;{\cdots}))\\ =1}$$ 여기서, $y_i$는 가뭄특성값을 $x_i$, $z_i$, $\cdots$는 기상인자를 나타낸다. $$p(y-q)={{\beta}(y-q)\;y{\geq_-}q \\ (1-{\beta})(q-y)\;y<q}$$ ${\beta}$는 quantile을 나타내며 0< ${\beta}$ <1범위를 갖는다.

• The Journal of Korean Academic Society of Nursing Education
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• v.14 no.1
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• pp.73-79
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• 2008

Purpose: This study was conducted to investigate the level of nursing professionalism and the factors affecting it, In addition, it can serve as a fundamental resource for development of appropriate strategies for nursing professionalism. Method: This study was performed on 370 nurses working at two university hospitals located in Seoul. Data was analyzed by Pearson correlation analysis, and stepwise multiple regression using SPSS Win 14.0. Result: The estimated regression model of nursing professionalism was statistically significant (F=199.89, p=.000). Major factors influencing nursing professionalism were observed as ability of job performance (${\beta}=.811$), job satisfaction (${\beta}=.140$), organizational commitment (${\beta}=.086$), and turnover intention (${\beta}=.084$). These four factors explained 67.9% of nursing professionalism. Conclusion: Based on the results of the study, further research is needed to investigate other factors affecting nursing professionalism.