• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.881-889
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• 2021

The purpose of this study is to descriptively reveal the demographic and business profile and personal-entrepreneurial characteristics in Manado, the capital of North Sulawesi, and secondly to associate these profiles and characters to their business progress. A sample size of 21 respondents was drawn - selected from those who warmly welcomed the interviewers for an open-ended structured questionnaire. SPSS 24 has been employed to descriptively reveal the sample distribution according to demographic factors and business entities and to determine the dominant factors affecting the progress of the business by testing the hypothesis on the association of variables under study using specified statistical analytical tools, such as regression analysis, especially stepwise regression formula, between specified dependent variables and independent variables and /or between all variables. The stepwise regression analysis has enabled the researcher to determine which variables are the most important reflecting the personal characteristics theorized as "locus of control": self-efficacy, needs for achievement, personal traits, and barriers to business progress The analysis reveals that the progress of business does have an association and is dependent on the source of capital and education, needs for achievement and locus of control.

• Journal of Korea Trade
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• v.26 no.1
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• pp.113-125
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• 2022

Purpose - The primary purpose of this study is to verify whether the target set out by the International Maritime Organization (IMO) for reducing carbon emissions from ships can be achieved by quantitatively analyzing the trends in technological advances of fuel oil consumption in the container shipping market. To achieve this purpose, several scenarios are designed considering various options such as eco-friendly fuels, low-speed operation, and the growth in ship size. Design/methodology - The vessel size and speed used in prior studies are utilized to estimate the fuel oil consumption of container ships and the pace of technological progress and Energy Efficiency Design Index (EEDI) regulations are added. A database of 5,260 container ships, as of 2019, is used for multiple linear regression and quantile regression analyses. Findings - The fuel oil consumption of vessels is predominantly affected by their speed, followed by their size, and the annual technological progress is estimated to be 0.57%. As the quantile increases, the influence of ship size and pace of technological progress increases, while the influence of speed and coefficient of EEDI variables decreases. Originality/value - The conservative estimation of carbon emission drawn by a quantitative analysis of the technological progress concerning the fuel efficiency of container vessels shows that it is not possible to achieve IMO targets. Therefore, innovative efforts beyond the current scope of technological progress are required.

• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.1-5
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• 2010

This paper deals with a method to measure the thickness of scale-layer, iron oxide formed on the surface of the rolling steel, using a dielectric lens antenna. The dielectric lens antenna has an independent characteristic with the frequency in the X-band and changes the spherical wave radiated from a horn antenna into a plane wave at the focusing point. Using this concept, we regard a scale-layer on the rolling steel as a dielectric-PEC(Perfect Electric Conductor) layer and apply a theoretical analysis of the normal-incident plane wave. To reduce the phase error arising from the use of the dielectric lens antenna, this paper utilizes a regression process algorithm. In comparison with the conventional iteration algorithm, the present algorithm led to a unique solution for the thickness of the scale-layer.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.547-554
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• 2016

This study considered how to estimate the deterioration rate of the track quality index, which represents track geometric irregularity. Most existing studies have used a simple linear regression and regarded the slope of the regression equation as the progress rate. In this paper, we present a Bayesian approach to estimate the track irregularity progress. This Bayesian approach has many advantages, among which the biggest is that it can formally include the prior distribution of parameters which can be derived from historic data or from expert experiences; then, the rate can be expressed as a probability distribution. We investigated the possibility of applying the Bayesian method to the estimation of the deterioration rate by comparing our bayesian approach to the conventional linear regression approach.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.1518-1524
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• 2010

Measures on poor process should be identified by reviewing analysis of planned progress and actual progress for successful performance of Process Control. However, the existing Process Control only performances follow-up measures on poor process but it cannot prevent poor process which is not occurred. To solve these problems, this study suggests the three types of methods of process prediction(Regression Analysis) by using a progress rate which consists of planned progress rate and Actual progress rate.

• Journal of Technology Innovation
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• v.15 no.2
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• pp.1-24
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• 2007

In this study, we estimated a CES production function for the Korean economy. We have found in the empirical results that the elasticity of the factor substitution is less than one and that the Korean economy exhibits labor-saving technological progress. In addition, we obtained the regression coefficient of R&D investment on technological change, i.e., the elasticity of R&D investment with respect to the technological change was 0.26% point. It implies that if R&D stock increases by 1%, labor efficiency increases 0.26% point through technological progress which is Hicksian non-neutral. It confirms that innovation-based growth strategy by increasing R&D investment would be effective on the one hand. Some policy consideration on the other might be needed for an increase in employment which is offset by technological progress.

• Korean Journal Plant Pathology
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• v.14 no.2
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• pp.150-156
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• 1998

To develop a model for prediction of turnip mosaic virus(TuMV) disease progress of Chinese cabbage based on weather information and number of TuMV vector aphids trapped in Taegwallyeong alpine area, data were statistically processed together. As the variables influenced on TuMV disease progress, cumulative portion(CPT) above 13$^{\circ}C$ in daily average temperature was the most significant, and solar radiation, duration of sunshine, vector aphids and cumulative temperature above $0^{\circ}C$ were significant. When logistic model and Gompertz model were compared by detemining goodness of fit for TuMV disease progress using CPT as independent variable, regression coefficient was higher in the logistic model than in the Gompertz model. Epidemic parameters, apparent infection rate and initial value of logistic model, were estimated by examining the relationship between disease proportion linearized by logit transformation equation, In(Y/Yf-Y) and CPT. Models able to describe the progression of TuMV disease were formulated in Y=100/(1+128.4 exp(-0.013.CPT.(-1(1/(1+66.7.exp(-0.11.day). Calculated disease progress from the model was in good agreement with investigated actual disease progress showing high significance of the coefficient of determination with 0.710.

• Journal of Preventive Medicine and Public Health
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• v.7 no.2
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• pp.299-304
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• 1974

The physical status (standing height, body weight, chest girth, sitting height, length of leg, length of thigh, thigh girth, length of crus, length of arm, brachial length, antebrachial girth and skinfold thickness) of 360 healthy middle and high school boys aged between 12 and 17 years in Taegu area was measured and evaluated by means of dispersion. For regression equation and coefficient ofidetermination of each status against standing height were computed. The growth progress of physical status had a tendency to be exponential and, generally, between 13 and 14 years of age the fastest progress was observed. The regression coefficient of body weight against standing height (0.90) was largest and that of skinfold thickness against standing height (0.09) was smallest. In general, the dimension of the regression coefficient was accordant with the dimension of respective physical status. Except in length of thigh and skinfold thickness, coefficient of determination of each physical status against standing height was almost 1 and the regression line could express the relation between standing height and each physical status very satisfactorily. But the regression curve was more desirable for the elucidation of the relation between standing height and skinfold thickness.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.643-655
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• 2004

L$_1$-estimator in the linear regression model is widely recognized to have superior robustness in the presence of vertical outliers. While the L$_1$-estimation procedures and algorithms have been developed quite well, less progress has been made with the hypothesis test in the multiple L$_1$-regression. This article suggests computer-intensive resampling approaches, jackknife and bootstrap methods, to estimating the variance of L$_1$-estimator and the scale parameter that are required to compute the test statistics. Monte Carlo simulation studies are performed to measure the power of tests in small samples. The simulation results indicate that bootstrap estimation method is the most powerful one when it is employed to the likelihood ratio test.