• Tribology and Lubricants
• /
• 제26권6호
• /
• pp.329-335
• /
• 2010

Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.

• Journal of Korea Artificial Intelligence Association
• /
• 제1권1호
• /
• pp.11-16
• /
• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Communications for Statistical Applications and Methods
• /
• 제29권6호
• /
• pp.629-640
• /
• 2022

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method.

• The Journal of Asian Finance, Economics and Business
• /
• 제8권2호
• /
• pp.1017-1023
• /
• 2021

This research aims to determine the effect of customers' threat emotion and price on the decision to purchase a certain smartphone product. This study uses a quantitative method with a type of descriptive and causal research. It employs non-probability sampling with purposive sampling, with 385 respondents to answer the questionnaires. Data analysis techniques used descriptive analysis and multiple linear regression analysis. Based on the results of descriptive analysis of emotion, price and purchasing decisions are in sync with each other. The results of multiple linear regression analysis techniques indicate the threat emotion and brand trust are influential against the positive decision to purchase smartphone products. The magnitude of the influence of emotions and price have simultaneous effect on purchasing decisions and other decision variables, which are not included in this study, also play minor role in determining purchase intention, such as product quality, brand image and others. Partially, threat emotion and brand trust have a positive effect toward purchasing decisions. The magnitude of the highest influence was the one of price, then followed by emotional threats. The findings of this study suggest that psychological and behavioral effects also play important roles in determining customers' purchase decision.

• 농촌의학ㆍ지역보건
• /
• 제33권2호
• /
• pp.221-231
• /
• 2008

Abstract - Objective: This study was conducted to investigate the effect of work conditions on job stress of social workers in Seoul. Method: For this survey, a self-reported questionnaire was administrated to 1,000 social workers working in all of organization for social welfare practice in Seoul. The number of responded questionnaires was 431. Multiple linear regression analysis was used for job stress as the dependent variables and control variables. Results: The result of multiple linear regression analysis indicated that regular rest breaks had significantly effect on job stress level but long working hours did not. When regular rest breaks was guaranteed job stress of social workers significantly lowered 8.4 point. In addition standardized regression coefficients and partial R2 of regular rest breaks was the highest score among the variables. Conclusion: This study suggests that it is the most important to guarantee regular rest breaks in the work schedule in order to alleviate job stress of social workers.

• 한국물환경학회지
• /
• 제23권3호
• /
• pp.385-390
• /
• 2007

Reliable flow measurement for dry season is very important to set up the in-stream flow exactly and total maximum daily load control program in the basin. Especially, in the points which tidal current effects are dominant because reliability of the low measurement decrease. The reliable measuring methods are needed. In this study, we analysis the water surface elevation difference of water surface elevation. Quantity relationship to consider tidal currents in these regions. It is known that tidal current effects from Nakdong river barrage are dominant in Samrangjin measuring station. We developed multiple regression equation with water surface elevation, quantity, and difference of water surface elevation and compared these results water measured rating curve. All of these regression equation including linear regression equation and log regression equation fits better measured data them existing water surface elevation quantity line and Among three equations, the log regression equation is best to represent the measured the rating curve in Samrangjin point. The log regression equation is useful method to obtain the quantity in the regions which tidal currents are dominant.

• 대한용접접합학회:학술대회논문집
• /
• 대한용접접합학회 2006년 추계학술발표대회 개요집
• /
• pp.271-273
• /
• 2006

This paper focuses on the developed empirical models for the prediction on top-bead width in GMA(Gas Metal Arc) welding process. Three empirical models have been developed: linear, curvilinear and an intelligent model. Regression analysis was employed fur optimization of the coefficients of linear and curvilinear model, while Genetic Algorithm(GA) was utilized to estimate the coefficients of intelligent model. Not only the fitting of these models were checked, but also the prediction on top-bead width was carried out. ANOVA analysis and contour plots were respectively employed to represent main and interaction effects between process parameters on top-bead width.

• Bulletin of the Korean Chemical Society
• /
• 제32권11호
• /
• pp.3865-3872
• /
• 2011

A quantitative structure-property relationship (QSPR) study was performed for the prediction of the absorption maxima of azobenzene dyes. The entire set of 191 azobenzenes was divided into a training set of 150 azobenzenes and a test set of 41 azobenzenes according to Kennard and Stones algorithm. A seven-descriptor model, with squared correlation coefficient ($R^2$) of 0.8755 and standard error of estimation (s) of 14.476, was developed by applying stepwise multiple linear regression (MLR) analysis on the training set. The reliability of the proposed model was further illustrated using various evaluation techniques: leave-many-out crossvalidation procedure, randomization tests, and validation through the test set.

• 산업경영시스템학회지
• /
• 제18권34호
• /
• pp.35-42
• /
• 1995

In the plastic extrusion process, product quality is influenced by work condition for temperature of cylinders and dies. The work conditions are various, so it is difficult to standardization of the work conditions. Therefore, the work conditions are depended on the workers of experience and skill. In the plastic extrusion process, it has five control heating points on the cylinder and three control heating points on the die. In addition, there is one control point on the extrusion process. It is extrusion speed. In this case, we don't know how these affect product quality. We structure the multiple linear regression equation with the temperature of cylinders and dies as independent variables and the product weight as dependent variable. We solve this equation using statistic computer package named Juse-Qcas.

• 한국안전학회지
• /
• 제27권3호
• /
• pp.161-166
• /
• 2012

This study deals with the accident of roundabout. The objective is to analyze the traffic accidents occurred in 3-legged and 4-legged roundabouts through the developed models. In developing the multiple linear regression models, this study uses the number of traffic accidents as a dependent variable and such the variables as geometric structures, traffic characters and others as the independent variables. The correlation and multicollinearity of variables were analyzed using SPSS17.0. The main results are as follows. First, R-square value of developed models were analyzed to be 0.851(3-leg) and 0.689(4-leg), respectively. Second, the independent variables in the 3-legged roundabout accident model were analyzed to be the traffic volume and number of crosswalk, and the variables in the 4-legged roundabouts were evaluated to be the traffic volume and signal. Finally, the paired t-test shows that the predicted values and observed values are not statistically different.