• Journal of Welding and Joining
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• v.17 no.4
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• pp.85-92
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• 1999

In GMAW, the spatter is generated because of the variation of the arc state. If the arc state is quantitatively assessed, the control method to make the spatter be reduced is able to develop. This study was attempted to develop the optimal model that could estimate the arc state quantitatively. To do this, the generated spatters was captured under the limited welding conditions, and the waveforms of the arc voltage and of the welding current were collected. From the collected waveforms, the waveform factors and their standard deviations were produced, and the linear and non-linear regression models constituted using the factors and their standard deviations are proposed to estimate the arc state. the performance test to the proposed models was practiced. Obtained results are as follow. From the results of correlation analysis between the factors and the amount of the generated spatters, the standard deviations of the waveform factors have more the multiple regression coefficients than the waveform factors. Because the correlation coefficient between T and {TEX}$T_{a}${/TEX}, and s[T] and s[{TEX}$T_{a}${/TEX}] was nearly one, it was found that these factors have the same effect to the spatter generation. In the regression models to estimate the arc state, it was fond that the linear and the non linear models were also consisted of similar factors. In addition, the linear regression model was assessed the optimal model for estimating the arc state because the variance of data was narrow and multiple regression coefficient was highest among the models. But in the welding conditions which the amount of the generated spatters were small, it was found that the non linear regression model had better the estimation performance for the spatter generation than the linear.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1387-1399
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• 2020

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.47-53
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• 2019

The idea of ensemble learning is to train multiple models, each with the objective to predict or classify a set of results. Most of the errors from a model's learning are from three main factors: variance, noise, and bias. By using ensemble methods, we're able to increase the stability of the final model and reduce the errors mentioned previously. By combining many models, we're able to reduce the variance, even when they are individually not great. In this paper we propose an ensemble model and applied it to classification problem. In iris, Pima indian diabeit and semiconductor fault detection problem, proposed model classifies well compared to traditional single classifier that is logistic regression, SVM and random forest.

• Journal of Korean Public Health Nursing
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• v.27 no.1
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• pp.113-128
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• 2013

Purpose: The purpose of this study was to investigate the degree of suicidal risk, meaning in life, and life respect in various ages of patients and identify factors influencing their life respect. Method: The participants were 229 patients in a public hospital who completed questionnaires. Data were analyzed using descriptive statistics, t-test, Fisher's exact test, ANOVA with Duncan post hoc test, and multiple regression. Results: There was a negative correlation between the meaning of life and life respect in the old age group (r=-.23, p=.02) and all subjects (r=-.14, p=.01) after controlling for age. Factors significantly influencing life respect were gender (${\beta}$=0.11, p=.050) and educational status (${\beta}$=-0.17, p=.022), and the multiple regression model explained 16.7% of the variance in all subjects (p<.001). In the early adulthood group, factors significantly influencing the life respect were gender (${\beta}$=0.18, p<.001) and suicidal thoughts (${\beta}$=0.21, p=.028), and the multiple regression model explained 6.8% of variance in all subjects (p=.001). Conclusion: The results of this study suggest that suicidal prevention and educational programs for increasing an appreciation of life should consider subject's characteristics, such as gender and educational status.

• 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.

• The Korean Journal of Pain
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• v.32 no.2
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• pp.120-128
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• 2019

Background: We aimed to explore the American College of Rheumatology (ACR) 1990 and 2011 fibromyalgia (FM) classification criteria's items and the components of Fibromyalgia Impact Questionnaire (FIQ) to identify features best discriminating FM features. Finally, we developed a combined FM diagnostic (C-FM) model using the FM's key features. Methods: The means and frequency on tender points (TPs), ACR 2011 components and FIQ items were calculated in the FM and non-FM (osteoarthritis [OA] and non-OA) patients. Then, two-step multiple logistic regression analysis was performed to order these variables according to their maximal statistical contribution in predicting group membership. Partial correlations assessed their unique contribution, and two-group discriminant analysis provided a classification table. Using receiver operator characteristic analyses, we determined the sensitivity and specificity of the final model. Results: A total of 172 patients with FM, 75 with OA and 21 with periarthritis or regional pain syndromes were enrolled. Two steps multiple logistic regression analysis identified 8 key features of FM which accounted for 64.8% of variance associated with FM group membership: lateral epicondyle TP with variance percentages (36.9%), neck pain (14.5%), fatigue (4.7%), insomnia (3%), upper back pain (2.2%), shoulder pain (1.5%), gluteal TP (1.2%), and FIQ fatigue (0.9%). The C-FM model demonstrated a 91.4% correct classification rate, 91.9% for sensitivity and 91.7% for specificity. Conclusions: The C-FM model can accurately detect FM patients among other pain disorders. Re-inclusion of TPs along with saving of FM main symptoms in the C-FM model is a unique feature of this model.

• 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).

• Journal of Korean Academy of Nursing
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• v.34 no.7
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• pp.1288-1297
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• 2004

Purpose: The purpose of this study was to investigate the factors influencing health promoting lifestyle in the elderly. Method: The subject of this study was 305 elderly person over the age of 60, living in rural and urban, Korea. For the analysis of collected data, descriptive statistics, t-test, analysis of variance and stepwise multiple regression were used for statistical analysis with SPSS statistical program. Results: The average item score for the health promoting lifestyle was 2.46, The higher score on the subscale was nutrition(2.65). The lowest score on the subscale were physical activity(2,36) and stress management(2,36). General characteristics showing statistically significant difference in health promoting lifestyle were age, residential district, live together spouse, education, religion and pocket money in the elderly. Stepwise multiple regression analysis revealed that the most powerful predictor of health promoting lifestyle in the elderly was prior related behavior(R2=.554). A combination of prior related behavior, perceived benefits of action, perceived self-efficacy, commitment to a plan of action, and interpersonal influences accounted for $64.3\%$ of the variance in health promoting lifestyle in the elderly, Conclusion: The factors influencing on health promoting lifestyle for elderly were prior related behavior, perceived benefits of action, perceived self-efficacy, commitment to a plan of action, and interpersonal influences.

• Korean Journal of Adult Nursing
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• v.18 no.4
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• pp.523-532
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• 2006

Purpose: The positive effects of health behaviors in older population are well recognized, but maintenance of health habits was more difficult than initiation. The purposes of the study were to identify predictors of health behavior based on motivation theories, and to analyze predicting power of motivational factors to explain health behaviors in older adults. Methods: The data were collected from older adults either institutionalized or living in the community. Total of 159 subjects with 72 years old in average were recruited for an interview. Hierarchical multiple regression analysis were utilized to analyze the data with age, residential type, and motivational variables. Results: The results of the multiple regression analysis showed that age and residential type explained 3% of variance in health behaviors (F=3.705, p=0.027). When motivational variables were entered, additional 56.9% of variance were explained by the model (F=33.275, p< 0.001). Among motivational variables, perceived benefits was the most important variable (${\beta}=0.346$, t=4.582, p<0.001), followed by self efficacy, emotional salience, and perceived barriers. Conclusion: Considering the importance of each motivational variable, the focus of intervention strategies to assist older adults to maintain health behaviors should be on modifiable and important motivational variables, such as self-efficacy, perceived benefits and barriers, and emotional salience.

• Korean Journal of Exercise Nutrition
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• v.14 no.2
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• pp.81-86
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• 2010

The purpose of this study was to identify the factors associated with child's Body Mass Index (BMI) and physical activity. The participants (n = 133) were Korean juveniles (3^rd and 4^th graders) and their parents. They completed a questionnaire packet including the SPARK (Sports, Play, and Active Recreation for Kids) survey and the parent equivalent survey. Correlation, multiple linear regression and binary logistic regression analyses were applied to identify the association between child's BMI and 10 factors of SPARK as predict or variables. 25.6% of the participants were classified as overweight (21.1%) or obesity (4.5%). 3 parental factors including mother's BMI and frequency of mother's and father's physical activity were identified as significant predictors of children's BMI. The 10 variables accounted for 28% of the variance (p<.01) in the linear regression model. These results provide insight into parental factors which are related to a child's BMI and physical activity. Parental role modeling which refers to parents' efforts to model an active lifestyle for children plays an important role.