• Journal of the Korean Statistical Society
• /
• v.7 no.2
• /
• pp.109-119
• /
• 1978

When a response surface by a seconde order polynomial regression model, the stationary point is obtained by solving simultaneous linear equations. But the point is a function of random variables. We can find a confidence region for this point as Box and Hunter provided. However, the confidence region is often too large to be useful for the experiments, and it is necessary to augment additional design points in order to obtain a satisfactory confidence region for the stationary point. In this note, the author suggests a method how to augment design points "eficiently", and shows the change of the confidence region of the estimated stationary point in a response surface.e surface.

• Journal of Korean Society for Quality Management
• /
• v.25 no.2
• /
• pp.102-111
• /
• 1997

In this paper I discuss a method of constructing the confidence region for the logistic response surface model. The construction involves a, pp.ication of a general fitting procedure because the log odds is linear in its parameters. Estimation of parameters of the logistic response surface model can be accomplished by maximum likelihood, although this requires iterative computational method. Using the asymptotic results, asymptotic covariance of the estimators can be obtained. This can be used in the construction of confidence regions for the parameters and for the logistic response surface model.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.2
• /
• pp.597-608
• /
• 2008

Marketing has been used the power of data and information technology in the pursuit of personal marketing of products and service to customers, based on their preferences and needs. We analyzed the performance of twenty six combined(RFM and Confidence) response modeling methods that were proposed by Zahavi and Levin(l997) and Sho, et al.(1999). As a result, we were able to increase about 3.5%p. forecasting accuracy of customers response through combination with confidence(C) that is able to consider characteristics of product than using the single RFM model that is practically the most widely used.

• Journal of Mechanical Science and Technology
• /
• v.15 no.8
• /
• pp.1143-1155
• /
• 2001

In order to reduce the expensive CPU time for design sensitivity analysis in dynamic response optimization, this study introduces the design sensitivities approximated within estimated confidence radius in dynamic response optimization with ALM method. The confidence radius is estimated by the linear approximation with Hessian of quasi-Newton formula and qualifies the approximate gradient to be validly used during optimization process. In this study, if the design changes between consecutive iterations are within the estimated confidence radius, then the approximate gradients are accepted. Otherwise, the exact gradients are used such as analytical or finite differenced gradients. This hybrid design sensitivity analysis method is embedded in an in-house ALM based dynamic response optimizer, which solves three typical dynamic response optimization problems and one practical design problem for a tracked vehicle suspension system. The optimization results are compared with those of the conventional method that uses only exact gradients throughout optimization process. These comparisons show that the hybrid method is more efficient than the conventional method. Especially, in the tracked vehicle suspension system design, the proposed method yields 14 percent reduction of the total CPU time and the number of analyses than the conventional method, while giving similar optimum values.

• Communications for Statistical Applications and Methods
• /
• v.8 no.3
• /
• pp.581-588
• /
• 2001

The $ED_{100p}$ is that value of the dose associated with 100p% response rate in the analysis of quantal response data. Brand, Pinnock, and Jackson (1973) studied the confidence bands of $ED_{100p}$ obtained by solving extremal values algebraically on the ellipsoid confidence region of the parameters in the simple logistic regression model. In this paper, we develope and illustrate a simpler method for obtaining confidence bands for $ED_{100p}$ based on the rectangular confidence region of parameters.

• Journal of the Korean Statistical Society
• /
• v.24 no.2
• /
• pp.551-562
• /
• 1995

We consider the iterated bootstrap method in regression model with heterogeneous error variances. The iterated wild bootstrap confidence intervla of the mean response is considered. It is shown that the iterated wild bootstrap confidence interval has coverage error of order $n^{-1}$ wheresa percentile method interval has an error of order $n^{-1/2}$. The simulation results reveal that the iterated bootstrap method calibrates the coverage error of percentile method interval successfully even for the small sample size.

• Journal of the Korean Statistical Society
• /
• v.30 no.4
• /
• pp.585-595
• /
• 2001

A second-order response surface model is often used to approximate the relationship between a response factor and a set of explanatory factors. In this article, we deal with canonical analysis in response surface models. For the interpretation of the geometry of second-order response surface model, standard errors and confidence intervals for the eigenvalues of the second-order coefficient matrix play an important role. If the confidence interval for some eigenvalue includes 0 or the estimate of some eigenvalue is very small (near to 0) with respect to other eigenvalues, then we are able to delete the corresponding canonical factor. We propose a formulation of criterion which can be used to select canonical factors. This criterion is based on the IMSE(=Integrated Mean Squared Error). As a result of this method, we may approximately write the canonical factors as a set of some important explanatory factors.

• Culinary science and hospitality research
• /
• v.10 no.1
• /
• pp.153-166
• /
• 2004

This study analyzed the positioning of family restaurants by image factors, as a result, customers divided into 5 groups from 7 restaurants. All customers realized similar images from subject restaurants, which told that those restaurants are within an intense competition. Strong points of each restaurant are as follows by factors; Outback and TGIF was confidence, Sizzler and Outback was response, Outback and Skylark was type, Bennigan's and TGIF was reliability, Bennigan's and Outback was reactivity. Weak points each restaurant are as follows by factors; Bennigan's and TonyRoma's were confidence and response, TonyRoma's and Marche was type, Sizzler and TonyRoma's was confidence, Sizzler and TonyRoma's was reactivity. The analysis on each restaurants' images by factors told that response and reactivity were above the average, confidence and reliability were around the average, type was the lowest value. It told that customers didn't give a high rating toward restaurants' food flavor, freshness, cleanliness, therefore restaurants need pay more attention to positioning strategies, Instead, customers gave a high rating toward service, accessibility, menu-variety, and parking-convenience, therefore, continuous improvement is required.

• Communications for Statistical Applications and Methods
• /
• v.10 no.3
• /
• pp.997-1005
• /
• 2003

We can use the logistic model for categorical data when the response variables are binary data. In this paper we consider the problem of constructing the confidence intervals for logistic model in I${\times}$J${\times}$2 contingency table. These constructions are simplified by applying logit transformation. This transforms the problem to consider linear form which called the logit model. After obtaining the confidence intervals for the logit model, the reverse transform is applied to obtain the confidence intervals for the logistic model.

• The Korean Journal of Applied Statistics
• /
• v.28 no.1
• /
• pp.75-82
• /
• 2015

We discuss confidence intervals for sensitive binomial attributes obtained by a direct question method and indirect question method. The Randomized Response Technique(RRT) by Warner (1965) is an indirect question method that uses a randomization device to reduce the response burden of respondents. We used the mean coverage probability (MCP), root mean squared error (RMSE), and mean expected width (MEW) to compare the confidence intervals by the two methods. The numerical comparisons indicated found that the MEW of RRT is too large and the RRT is so conservative that the MCP exceeds a nominal level(${\alpha}$); therefore, it is necessary to complement these problem in order to increase the utility of the indirect question method.