• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.447-461
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• 2018

Domestic film industry sales are increasing every year. Theaters are the primary sales channels for movies and the number of audiences using the theater affects additional selling rights. Therefore, the number of audiences using the theater is an important factor directly linked to movie industry sales. In this paper we consider a hybrid model that combines a multiple linear regression model and the Bass model to predict the audience numbers for a specific day. By combining the two models, the predictive value of the regression analysis was corrected to that of the Bass model. In the analysis, three films with different release dates were used. All subset regression method is used to generate all possible combinations and 5-fold cross validation to estimate the model 5 times. In this case, the predicted value is obtained from the model with the smallest root mean square error and then combined with the predicted value of the Bass model to obtain the final predicted value. With the existence of past data, it was confirmed that the weight of the Bass model increases and the compensation is added to the predicted value.

• The Journal of the Acoustical Society of Korea
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• v.30 no.1
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• pp.56-62
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• 2011

In the present study, the phase velocities of guided ultrasonic waves such as the first arriving signal (FAS) and the slow guided wave (SGW) propagating along the long axis on the 12 tubular cortical bone samples in vitro were measured and their correlations with the cortical thickness were investigated. The phase velocities of the FAS and the SGW were measured by using the axial transmission method in air with a pair of unfocused ultrasonic transducers with a diameter of 12.7 mm and a center frequency of 200 kHz. The phase velocity of the FAS measured at 200 kHz exhibited a very high negative correlation with the cortical thickness and that of the SGW arriving after the FAS showed a high positive correlation with the cortical thickness. The simple and multiple linear regression models with the phase velocities of the FAS and the SGW as independent variables and the cortical thickness as a dependent variable revealed that the coefficient of determination of the multiple linear regression model was higher than those of the simple linear regression models. The phase velocities of the FAS and the SGW measured at 200 kHz on the 12 tubular cortical bone samples were, respectively, consistent with those of the S0 and the A0 Lamb modes calculated at 200 kHz on the cortical bone plate.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.335-345
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• 2020

This paper deals with the problem of estimating the log-transformed linear regression model to fit actual battle data from the Ardennes Campaign of World War II into the Lanchester model. The problem of determining a global solution for parameters and multicollinearity problems are identified and modified by examining the results of previous studies on data. The least squares method requires attention because a local solution can be found rather than a global solution if considering a specific constraint or a limited candidate group. The method of exploring this multicollinearity problem can be confirmed by a statistic known as a variance inflation factor. Therefore, the Lanchester model is simplified to avoid these problems, and the combat power attrition rate model was proposed which is statistically significant and easy to explain. When fitting the model, the dependence problem between the data has occurred due to autocorrelation. Matters that might be underestimated or overestimated were resolved by the Cochrane-Orcutt method as well as guaranteeing independence and normality.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.181-194
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• 2017

This study aims to develop a regression model for forest volume estimation using field-collected forest inventory information and airborne LiDAR data. The response variable of the model is forest stem volume, was measured by random sampling from each individual plot of the 30 circular sample plots collected in Bonghwa-gun, Gyeong sangbuk-do, while the predictor variables for the model are Height Percentiles(HP) and Height Bin(HB), which are metrics extracted from raw LiDAR data. In order to find the most appropriate model, the candidate models are constructed from simple linear regression, quadratic polynomial regression and multiple regression analysis and the cross-validation tests were conducted for verification purposes. As a result, $R^2$ of the multiple regression models of $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}$ among the estimated models was the highest at 0.509, and the PRESS statistic of the simple linear regression model of $HP_{25}$ was the lowest at 122.352. $HB_{5-10}$, $HB_{15-20}$, $HB_{20-25}$, and $HBgt_{25}-based$ models, thus, are comparatively considered more appropriate for Korean forests with complicated vertical structures.

• Proceedings of the Korean Printing Society Conference
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• 2000.12a
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• pp.0.2-0
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• 2000

Most important step for the color matching in scanner is the color coordinate transformation from the scanner RGB space to device independent uniform color space. A variety of color calibration technologies have been developed for input device. Linear or nonlinear matrices have been conveniently applied to correct the color filter\`s mismatch with color matching function in scanners. The color matching accuracy is expected to be further improved when the nonlinear matrices are optimized into subdivided smaller color spaces than in single matrix of the entire color space. This article proposed the scanner calibration method using subspace division regression analysis and it were compared with conventional method.

• Journal of the Korean Graphic Arts Communication Society
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• v.19 no.1
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• pp.4-16
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• 2001

Most important step for the color matching in scanner is the color coordinate transformation from the scanner RGB space to device independent uniform color space. A variety of color calibration technologies have been developed for input device. Linear or nonlinear matrices have been conveniently applied to correct the color filter's mismatch with color matching function in scanners. The color matching accuracy is expected to be further improved when the nonlinear matrices are optimized into subdivided smaller color spaces than in single matrix of the entire color space. This article proposed the scanner calibration method using subspace division regression analysis and it were compared with conventional method.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.4
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• pp.426-434
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• 2020

This paper studies the impact of the maintenance time of anti-ship missile Harpoon on operational availability with real field data. The Harpoon maintenance simulation model is developed as a testbed for identifying the optimal inventory levels on operational availability. Using multiple linear regression analysis and integer programming, the optimal inventory levels of essential assemblies are suggested. Finally, the result of sensitivity analysis shows the quantitative impact of maintenance time on operational availability and inventory costs. The authors believe that this quantitative analysis can support policy decisions to decrease maintenance time of missiles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.45-50
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• 2012

This paper on analysis of roundness error after boring turning of Al-6061 materials with CNC lathe. Experiment applying turning parameters is based on experimental design method. A design and analysis of experiments is conducted to study the effects of these parameters on the roundness error using the S/N ratio and analysis of ANOVA. Multiple linear regression analysis is applied to compare experimental with predicted data in consideration of roundness error. To fixation pressure and the opening which are a turning parameter, the cutting depth and feed speed respected the objective attainment of dissertation and to be applied the result they investigated.

• Corrosion Science and Technology
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• v.19 no.6
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• pp.302-309
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• 2020

This study aimed to predict the pitting corrosion characteristics of AL-6XN super-austenitic steel using multiple linear regression. The variables used in the model are degree of sensitization, temperature, and pH. Experiments were designed and cyclic polarization curve tests were conducted accordingly. The data obtained from the cyclic polarization curve tests were used as training data for the multiple linear regression model. The significance of each factor in the response (critical pitting potential, repassivation potential) was analyzed. The multiple linear regression model was validated using experimental conditions that were not included in the training data. As a result, the degree of sensitization showed a greater effect than the other variables. Multiple linear regression showed poor performance for prediction of repassivation potential. On the other hand, the model showed a considerable degree of predictive performance for critical pitting potential. The coefficient of determination (R2) was 0.7745. The possibility for pitting potential prediction was confirmed using multiple linear regression.

• The korean journal of orthodontics
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• v.39 no.2
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• pp.112-119
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• 2009

Objective: To develop a mixed dentition analysis method in consideration of the normal variation of tooth sizes. Methods: According to the tooth-size of the maxillary central incisor, maxillary 1st molar, mandibular central incisor, mandibular lateral incisor, and mandibular 1st molar, 307 normal occlusion subjects were clustered into the smaller and larger tooth-size groups. Multiple regression analyses were then performed to predict the sizes of the canine and premolars for the 2 groups and both genders separately. For a cross validation dataset, 504 malocclusion patients were assigned into the 2 groups. Then multiple regression equations were applied. Results: Our results show that the maximum errors of the predicted space for the canine, 1st and 2nd premolars were 0.71 and 0.82 mm residual standard deviation for the normal occlusion and malocclusion groups, respectively. For malocclusion patients, the prediction errors did not imply a statistically significant difference depending on the types of malocclusion nor the types of tooth-size groups. The frequency of prediction error more than 1 mm and 2 mm were 17.3% and 1.8%, respectively. The overall prediction accuracy was dramatically improved in this study compared to that of previous studies. Conclusions: The computer aided calculation method used in this study appeared to be more efficient.