• Journal of the Korean Statistical Society
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• v.26 no.3
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• pp.365-382
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• 1997

In this paper we study the behaviors of the generalized Durbin-Watson (DW) statistics when the nonstationary seasonal time series regression model is misspecified. It is observed that when the series is seasonally integrated the generalized DW statistic for the seasonal period order autocorrelation converges in probability to zero while teh generalized DW statistic for the first order autocorrelation has nondegenerate asymptotic distribution. When the series is regularly and seasonally integrated the generalized DW for the first order autocorrelation still converges in probability to zero.

• Journal of the Korean Statistical Society
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• v.27 no.1
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• pp.57-66
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• 1998

In the analysis of time series it is an important issue to determine whether a time series under study is stationary. For the test of the stationary of the time series the Dickey-Fuller (DF) type tests have been mainly used. In this paper, we consider the regular unit root tests and seasonal unit root tests based on the generalized Durbin-Watson (DW) statistics when the errors are independent. The limiting distributions of the proposed DW-type test statistics are the functionals of standard Brownian motions. We also obtain the finite distributions and powers of the DW-type test statistics and compare the performances with the DF-type tests. It is observed that the DW-type test statistics have good behaviors against the DF-type test statistics especially in the nonzero (seasonal) mean model.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.597-612
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• 2001

We have developed a \

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.559-564
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• 2016

The Durbin-Watson (DW) test in regression model and the Ljung-Box (LB) test in ARMA (autoregressive moving average) model are typical examples of correlated error tests. The DW test is used for detecting autocorrelation of errors using the residuals from a regression analysis. The LB test is used for specifying the correct ARMA model using the first some sample autocorrelations based on the residuals of a tted ARMA model. In this article, simulations with four data generating processes have been carried out to evaluate their performances as correlated error tests. Our simulations show that the DW test is severely dependent on the assumed AR(1) model but isn't sensitive enough to reject the misspecified model and that the LB test reports lackluster performance in general.

• 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 Korea Entertainment Industry Association
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• v.15 no.5
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• pp.99-113
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• 2021

This study to verify the influence of the macroeconomic factors to sportainment industry and also to find the value of use. For this, 'Dow Jones Industrial Average (DJIA)', 'West Texas intermediate (WTI)', and 'Gold Price (GP)' were selected from macroeconomic factors, and the 'Stock Price' of NIKE and Adidas for sportainment industry factor. The transaction data for 20 years (5,285 trade days) were analyzed through a two-step extraction process. Durbin-Watson regression analysis was performed to prove the influence and predict. From these analyses, the first, the Macroeconomics factors were found to have a significant effect on the sportainment industry. The second, each different levels of regression equations were found by the time setting, the environmental characteristics of each time period, and mutual relation between factors. Finally, it was found that the regression equation between specific period can be used for the future prediction in sportainment industry.

• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.579-583
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• 2012

The Design of Experiment (DOE) is a most practical technique when establishing an optimal condition for production technology in Six Sigma innovation project. This research proposes the assessment of properties of error terms, such as normality, equal variance, unbiasedness and independence. The properties of six nonparametric ranking techniques for checking normality assumption are discussed as well as run test which is used to identify the randomness, and to check unbiased assumption. Furthermore, Durbin-Watson (DW) statistics and ARIMA (p,d,q) process are discussed to identify the serial correlation.

• Industry Promotion Research
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• v.2 no.1
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• pp.69-75
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• 2017

In this study, 62 questionnaires were surveyed from November 3 to November 25, 2016 for workers who received more than one skill improvement training at Korea Polytechnic University. Factor analysis was done by removing 11 items out of 27 items and using the final 16 items, five factors were derived and the validity was confirmed with significance probability p = .000. Durbin-Watson values were 1.787 and 1.780, respectively. The results of this study were as follows: First, the effects of skill enhancement training on job attitude of Korea Polytechnic University were found as follows. First, the effects on job satisfaction were consistency of contents (p <.05, ${\beta}=.434$) (P <.05, p = .328), and the sense of belonging was significant (p <.05, p = .338). Second, the correlation between the skill improvement training and the turnover attitude shows that the efficiency of method - job satisfaction (.311), efficiency of method - clarity of purpose (.350), efficiency of method - suitability of content (.771) - content fit (.467), job satisfaction - content fit (.191), but the sense of belonging was not correlated. Third, the results of the difference in perception of turnover attitude according to years of service of technical improvement training at Korea Polytechnic University did not have a statistically significant effect. In this study, it is meaningful to investigate the effect of the skill improvement training on the attitude of turnover and its effectiveness.

• Journal of the Korean housing association
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• v.21 no.2
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• pp.59-67
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• 2010

The maintenance cost plays a important role to plan the scale of the apartment housing such as a number of household, building area and building type. Therefore, it is required to forecast the cost considering various maintenance characteristics. The maintenance characteristics are floor area, number of household, heating type, site area and etc.. In addition, the maintenance cost are classified into 5 area. These are a personal expense, facility maintenance cost, energy and water cost, insurance and sanitary cost. These five cost area are related with various characteristics and brought up the estimation model using the stepwise multiple regression analysis. The energy and heating cost share over the 50% in the total cost and the personal expense cost shares about 40%. The personal expense cost per area is 5,272 won/$m^2{\cdot}yr$ irregardless of heating type and the district heating type is a higher cost than other type. In facility maintenance cost, the central heating type is 2,015 won/$m^2{\cdot}yr$ and higher than other type. The estimation models have good statistics in each model. Most of the model have a determination coefficient over 0.7 and Durbin Watson value between 1.5 and 2.5.

• Journal of Korean Society for Quality Management
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• v.24 no.4
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• pp.190-206
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• 1996

In case measurements are made on units of production in time order, it is reasonable to expect that the measurement errors will sometimes be first order autocorrelated, and a technique to test such autocorrelation is required to give good control of the productive process. Tool-wear process provide an example for which regression model can sometimes be useful in modeling and controlling the process. For the control of such process, we present a simple method for testing first order autocorrelation in regression errors. The method is based on Bayesian test method via Bayes factor and derived by observing that in general, a Bayes factor can be written as the product of a quantity called the Savage-Dickey density ratio and a correction factor ; both terms are easily estimated from Gibbs sampling technique. Performance of the method is examined by means of Monte Carlo simulation. It is noted that the test not only achieves satisfactory power but eliminates the inconvenience occurred in using the well-known Durbin-Watson test.