• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.219-225
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• 1998

The parameters in linear models with censored normal responses are usually estimated by the iterative maximum likelihood and least square methods. However, the iterative least square method is simple but hardly has theoretical justification, and the iterative maximum likelihood estimating equations are complicatedly derived. In this paper, we justify these methods via Wedderburn (1974)'s quasi-likelihood approach. This provides an explicit justification for the iterative least square method and also directly the iterative maximum likelihood method for estimating the regression coefficients.

• Architectural research
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• v.19 no.4
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• pp.95-99
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• 2017

Recently, it is becoming increasingly difficult to ignore carbon emission implication of building operations due to the significant rate of energy usage in buildings. In the building sector, our normal expectation implies that large building floor area induces more greenhouse gas (GHG) emission. In this research, the correlation between building total floor areas and GHG emission was explored by simple linear regression and analyzing the yielded residuals for confirming this seemingly obvious conjecture. By looking at the generated regression lines drawn based on the data sets representing public facilities in Ontario, Canada, we were able to confirm that carbon emission rate shows a proportional increase or decrease depending on the total floor area of buildings as has been implied as a conjecture. Some buildings were found to emit significantly large and small amount of GHG, and we addressed potential reasons why those buildings show the deviation from the confirmed proportional interrelation between a building's total floor area and the amount of GHG emission.

• Preventive Nutrition and Food Science
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• v.13 no.2
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• pp.104-111
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• 2008

As a way to account for the variability of the primary model parameters in the secondary modeling of microbial growth, three different regression approaches were compared in determining the confidence interval of the temperature-dependent primary model parameters and the estimated microbial growth during storage: bootstrapped regression with all the individual primary model parameter values; bootstrapped regression with average values at each temperature; and simple regression with regression lines of 2.5% and 97.5% percentile values. Temperature dependences of converted parameters (log $q_o$, ${\mu}_{max}^{1/2}$, log $N_{max}$) of hypothetical initial physiological state, maximum specific growth rate, and maximum cell density in Baranyi's model were subjected to the regression by quadratic, linear, and linear function, respectively. With an advantage of extracting the primary model parameters instantaneously at any temperature by using mathematical functions, regression lines of 2.5% and 97.5% percentile values were capable of accounting for variation in experimental data of microbial growth under constant and fluctuating temperature conditions.

• Nutrition Research and Practice
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• v.16 no.6
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• pp.745-754
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• 2022

BACKGROUND/OBJECTIVES: The incidence of cardiovascular diseases (CVDs) has increased worldwide. Although a low serum vitamin D level is known to be associated with the risk of CVD, the mechanism is not well understood yet. The aim of this study was to determine the relationship of serum 25-hydroxyvitamin D₃ (25[OH]D) with homocysteine and apolipoprotein B (ApoB). SUBJECTS/METHODS: Of 777 subjects recruited from one health promotion center for routine heath exam from January 2010 to December 2016, 518 subjects were included in this study. Serum 25(OH)D, serum homocysteine, and other metabolic parameters including ApoB were analyzed. Simple and partial correlations were carried out after adjustments. Simple linear regression analysis was used for precise correlation of parameters. Multivariate regression analysis was done to know which factor (serum homocysteine or ApoB) was more related to serum 25(OH)D after adjustments. Finally, logarithms of homocysteine concentrations according to tertiles of serum 25(OH)D were compared. RESULTS: After sex and age adjustments, serum 25(OH)D showed negative correlations with serum homocysteine (r' = -0.114) and ApoB (r' = -0.098). In simple linear regression analysis, serum 25(OH)D showed a significant negative correlation with ApoB (P = 0.035). However, in multivariate regression analysis, serum 25(OH)D was significantly associated with serum homocysteine after adjustments (P = 0.022). In addition, serum homocysteine concentration was significantly high in the lowest 25(OH)D group (P = 0.046). CONCLUSION: Serum 25(OH)D concentration showed a stronger negative association with serum homocysteine than with ApoB.

• Journal of the Korean Institute of Landscape Architecture
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• v.31 no.3
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• pp.74-82
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• 2003

Under the assumption that the temperature of one location is closely related to land use characteristics around that location, this study is carried out to assess the impact of urban land use patterns on air temperature. In order to investigate the relationship, GIS techniques and statistical analyses are utilized, after spatially connecting urban land use data in Seoul Metropolitan Area with atmospheric data observed at Automatic Weather Stations (AWS). The research method is as follows: (1) To find out important land use factors on temperature, simple linear regressions for a specific time period (pilot study) are conducted with urban land use characteristics, (2) To make a final model, multiple regressions are carried out with those factors and, (3) To verify that the final model could be appled to explain temperature variations beyond the period, the model is extensively used for 5 different time periods: 1999 as a whole; summer in 1999; 1998 as a whole; summer in 1998; August in 1998. The results of simple linear regression models in the pilot study show that transportation facilities and open space area are very influential on urban air temperature variations, which explain 66 and 61 percent of the variations, respectively. However, the other land use variables (residential, commercial, and mixed land use) are found to have weak or insignificant relationship to the air temperatures. Multiple linear regression with the two important variables in the pilot study is estimated, which shows that the model explains 75 percent of the variability in air temperatures with correct signs of regression coefficients. Thus, it is empirically shown that an increase in open space and a decrease in transportation facilities area can leads to the decrease in air temperature. After the final model is extensively applied to the 5 different time periods, the estimated models explain 68 ∼ 75 percent of the variations in the temperatures is significant regression coefficients for all explanatory variables. This result provides a possibility that one air temperature model for a specific time period could be a good model for other time periods near to the period. The important implications of this result to lessen high air temperature we: (1) to expand and to conserve open space and (2) to control transportation-related factors such as transportation facilities area, road pavement and traffic congestion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.137-145
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• 2020

Various elements of Fabrication (FAB), mass production of existing products, new product development and process improvement evaluation might increase the complexity of production process when products are produced at the same time. As a result, complex production operation makes it difficult to predict production capacity of facilities. In this environment, production forecasting is the basic information used for production plan, preventive maintenance, yield management, and new product development. In this paper, we tried to develop a multiple linear regression analysis model in order to improve the existing production capacity forecasting method, which is to estimate production capacity by using a simple trend analysis during short time periods. Specifically, we defined overall equipment effectiveness of facility as a performance measure to represent production capacity. Then, we considered the production capacities of interrelated facilities in the FAB production process during past several weeks as independent regression variables in order to reflect the impact of facility maintenance cycles and production sequences. By applying variable selection methods and selecting only some significant variables, we developed a multiple linear regression forecasting model. Through a numerical experiment, we showed the superiority of the proposed method by obtaining the mean residual error of 3.98%, and improving the previous one by 7.9%.

• Journal of Korean Tunnelling and Underground Space Association
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• v.10 no.3
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• pp.233-243
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• 2008

A tunnel behavior predicted in the investigation and design stage is often different from its actual behavior due to mainly the complexity of ground conditions. In a tunnel construction, therefore, it is necessary to ensure the stability of the tunnel by predicting the behaviors of the ground and the supports through observations and measurements, and modifying immediately excavation and reinforcing methods when necessary. To do so, it is important to be able to predict the final tunnel behavior based on the initial tunnel behavior as early as possible. In this study, the correlations were obtained between the initial and the final convergence by analyzing statistically the convergence measurement data, collected from two domestic road tunnels under construction using NATM. In order to estimate the unknown displacements, occurred during the period between the excavation and the first measurement, two methods were used - one is the method by means of regression analysis using a modified exponential function and the other the method by a simple linear regression analysis using the data measured within the distance from tunnel face equal to the tunnel diameter (D). Finally, the relationships were obtained between the initial and final convergence, including the non-measured displacements estimated from the two different methods, by performing linear regression analyses. The regression analysis results showed that there are clear linear relationships between the initial and final convegence and the difference between the two linear regression equations was not that large for when using the exponential function and the simple linear function to estimate the non-measured displacements.

• The Journal of Engineering Geology
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• v.24 no.1
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• pp.101-110
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• 2014

This study presents the results of a regression analysis of the point-load strength ($I_{s(50)}$) and the uniaxial compressive strength (UCS) of granites in Korea. The regression was carried out for three cases using the least-squares method, reclassifying the granite samples based on their physical properties. The first regression analysis through the origin according to the weathering grade did not give a result with a sufficient degree of confidence, due to the small number of samples. However, the general trend of the correlation between UCS and $I_{s(50)}$according to weathering grade shows that the slope of the linear regression for weathered granite is steeper than that for fresh granite. The second analysis was a simple linear regression for all the granite samples using the least-squares method as well as a linear regression using the bootstrap resampling method in order to increase the confidence level and the accuracy of the regression results. The third regression considered the average strength of granite groups reclassified according to physical properties. These linear regression analyses yielded linear regression equations with slopes of 14 and small standard deviations being similar to values reported in previous studies on Korean granites, but whose intercept values range from 16 to 43 and have a larger standard deviation than those of the present study. In conclusion, it would be advisable to estimate UCS from $I_{s(50)}$, considering the error range derived from the deviation of the regression equations.

• Journal of Korean Society for Quality Management
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• v.24 no.2
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• pp.128-141
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• 1996

The advent of the quality-improvement movement caused a great expansion in the use of statistically designed experiments in industry. The regression method is often used for the analysis of data from such experiments. However, the data for a quality characterstic often takes the form of counts or the ratio of counts, e.g. fraction of defectives. For such data the analysis using generalized linear models is preferred to that using the simple regression model. In this paper we introduce the generalized linear model and show how it can be used for the analysis of non-normal data from quality-improvement experiments.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.533-541
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• 2013

In this study, a simple principal component analysis using Lasso is proposed. This method consists of two steps. The first step is to compute principal components by the principal component analysis. The second step is to regress each principal component on the original data matrix by Lasso regression method. Each of new principal components is computed as the linear combination of original data matrix using the scaled estimated Lasso regression coefficient as the coefficients of the combination. This method leads to easily interpretable principal components with more 0 coefficients by the properties of Lasso regression models. This is because the estimator of the regression of each principal component on the original data matrix is the corresponding eigenvector. This method is applied to real and simulated data sets with the help of an R package for Lasso regression and its usefulness is demonstrated.