• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.589-597
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• 2021

In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.107-112
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• 2011

Recently, climate change around the world due to global warming has became an important issue and damages by climate change have a bad effect on human life. Changes of Sea Surface Temperature(SST) is associated with natural disaster such as Typhoon and El Nino. So we predicted daily future SST using Statistical Downscaling Method and CGCM 3.1 A1B scenario. 9 points of around Korea peninsular were selected to predict future SST and built up a regression model using Multiple Linear Regression. CGCM 3.1 was simulated with regression model, and that comparing Probability Density Function, Box-Plot, and statistical data to evaluate suitability of regression models, it was validated that regression models were built up properly.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.387-392
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• 1999

In this paper, we attempted to eatablish questionnaire items for evaluation of residential satisfaction level by factor analysis, and the model was developed as a function of primary component of questionnaire items. For development of residential satisfaction model, items are selected by factor analysis adn regression coefficient is estimated by the multiple linear regression analysis.

• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.453-467
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• 2023

Purpose: This study investigated the cumulative effects of depressive symptoms on cognitive function over time in community-dwelling older adults. Methods: Data were investigated from 2,533 community-dwelling older adults who participated in the Korean Longitudinal Study of Aging (KLoSA) from the 5th (2014) to the 8th wave (2020). The association between cumulative depressive symptoms and cognitive function was identified through multiple regression analysis. Results: When the multiple regression analysis was conducted from each wave, the current depressive symptoms scores and cognitive function scores were negatively associated, regardless of the waves (B5th = - 0.26, B6th = - 0.26, B7th = - 0.26, and B8th = - 0.27; all p < .001). Further, when all the previous depressive symptoms scores were added as explanatory variables in the 8th wave, the current one (B8th = - 0.09, p < .001) and the previous ones (B5th = - 0.11, B6th = - 0.09, and B7th = - 0.13; all p < .001) were also negatively associated with the cognitive function score. The delta R², which indicates the difference between the model's R² with and without the depressive symptoms scores, was greater in the model with all the previous and current depressive symptoms scores (6.4%) than in the model with only the current depressive symptoms score (3.6%). Conclusion: Depressive symptoms in older adults have a long-term impact. This results in an accumulated adverse effect on the cognitive function. Therefore, to prevent cognitive decline in older adults, we suggest detecting their depressive symptoms early and providing continuous intervention to reduce exposure to long-term depressive symptoms.

• Journal of Korean Society on Water Environment
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• v.30 no.1
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• pp.8-15
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• 2014

In this study, regression equation was analyzed to estimate non-point source (NPS) pollutant loads in orchard area. Many factors affecting the runoff of NPS pollutant as precipitation, storm duration time, antecedent dry weather period, total runoff density, average storm intensity and average runoff intensity were used as independent variables, NPS pollutant was used as a dependent variable to estimate multiple regression equation. Based on the real measurement data from 2008 to 2012, we performed correlation analysis among the environmental variables related to the rainfall NPS pollutant runoff. Significance test was confirmed that T-P ($R^2=0.89$) and BOD ($R^2=0.79$) showed the highest similarity with the estimated regression equations according to the NPS pollutant followed by SS and T-N with good similarity ($R^2$ >0.5). In the case of regression equation to estimate the NPS pollutant loads, regression equations of multiplied independent variables by exponential function and the logarithmic function model represented optimum with the experimented value.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.77-84
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• 1998

ARES plot (Cook and Weisberg, 1987) idea is applied to a multiple regression model in which the relation between a response variable and some independent variable is nonlinear. This method is expected to show the impact on the function to which and independent variable should be transformed, as a variable is smoothly added to the model.

• PNF and Movement
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• v.12 no.2
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• pp.123-132
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• 2014

Purpose: The discovery of mirror neuron system may positively affect functional recovery; therefore, rehabilitation is needed that is practical for use in clinical settings. The purpose of this study was to examine the effect of action observation training on upper motor function in people who had suffered strokes. Methods: Three elderly patients with stroke, aged to years, were recruited from a stroke rehabilitation center. A nonconcurrent, multiple baseline subject approach was taken, with an A-B-A treatment single-subject experimental design, and the experiment was conducted for 3 weeks. The action observation training was repeated 5 times in 5 days during the intervention period. The arm function, including WMFT, BBT, and grip and pinch strength, was evaluated in each subject 5 times during the baseline period, the intervention period, and the baseline regression period. Results: The results of the evaluation for each subject were presented as mean values and video graphs. The WMFT scores of 2 subjects were improved during the intervention period in comparison with the baseline period, and this improvement was maintained even during the regression baseline period. The BBT and the grip and pinch strength were not improved. Conclusion: Based on these results, we suggest that the action observation training for 5 sessions was effective in improving upper limb function of stroke patients but was not effective in improving hand dexterity or grip and pinch strength.

• Journal of Korean society of Dental Hygiene
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• v.19 no.1
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• pp.65-78
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• 2019

Objectives: This study was conducted to characterize the impact of cognitive function and oral health status on mastication in senior citizens, ${\geq}65$ years of age, using senior centers in the city of Wonju, South Korea. Methods: A cross-sectional study consisting of a simple oral examination and survey questionnaires was performed in 154 individuals. General characteristics, subjective masticatory function, objective masticatory function, cognitive function, and oral health status were collected as variables. Correlation and multiple linear regression analyses were conducted. A p-value of <0.05 was considered to be statistically significant. Results: The subjective masticatory function was scored using the 5-point Likert scale. When subjective masticatory function was analyzed in groups according to cognitive function, the mean subjective masticatory function scores were 4.31, 4.09, and 3.29 in the normal group (cognitive score of ${\geq}16$), suspected dementia group (cognitive score of 1215), and mild dementia group (cognitive score of ${\leq}11$), respectively. Thus, subjective masticatory function decreased along with decreasing cognitive function. When cognitive function, subjective masticatory function, and objective masticatory function were compared with indicators of oral health status (number of functional teeth, oral dryness), subjective masticatory function exhibited a significant positive correlation with objective masticatory function (r=0.635, p<0.01), cognitive function (r=0.292, p<0.01), and total number of functional teeth, including prosthetic appliances (dentures) (r=0.305, p<0.01). According to the regression analysis, age, sex, number of functional teeth, and cognitive function affected subjective masticatory function. Conclusions: The results of this study revealed that age, sex, number of functional teeth, and cognitive function affected subjective masticatory function, whereas oral dryness did not. Therefore, dental professionals must consider subjective masticatory function when providing oral care in senior patients with low cognitive function.

• The Korean Journal of Rehabilitation Nursing
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• v.19 no.2
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• pp.71-81
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• 2016

Purpose: The purpose of this study was to explore the influence of cognitive function and depressive symptoms on instrumental activities of daily living (IADL) in addition to identify the factors associated with IADL in community-dwelling older adults. Methods: This was a descriptive study with cross-sectional design. Data were collected from July 2013 to June 2014. A total of 143 participants without dementia, depression and disability were enrolled in this study. Cognitive function was measured using Seoul verbal learning test (SVLT), digital span (forward/backward) and fist-edge-palm test. The Korean-IADL and Short Geriatric Depression Scale (S-GDS) were used. Data analysis was performed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient, and hierarchical regression. Results: Overall, a multiple regression model revealed that approximately 27.4% of total variability in IADL in the sample of community-dwelling older adults could be explained by the significant 12 variables in this model ($R^2=0.274$, F=5.467, p<.001). Age, having religion and cognitive function were the predictors of IADL in community-dwelling older adults. Conclusion: This study suggest that we need to monitor cognitive function in older to maintain the ability for IADL in older adults. Also, individualized program for improving older adults' IADL should be included in nursing intervention.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.