• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.73-81
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• 2000

The variance inflation factor can be expressed by the square of the ratio of t-statistics associated with slopes of partial regression and partial residual plots. Disagreement of two sides in the interpretation can be occurred, and we analyze it with some illustrations.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.303-309
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• 1995

Many measures to detect multicollinearity in linear regression have been proposed in statistics and numerical analysis literature. Among them, condition number and variance inflation factor(VIF) are most popular. In this study, we give new interpretations of condition number and VIF in linear regression, using geometry on the explanatory space. In the same line, we derive natural measures of condition number and VIF for logistic regression. These computer intensive measures can be easily extended to evaluate multicollinearity in generalized linear models.

• Journal of the Korean Statistical Society
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• v.12 no.1
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• pp.36-45
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• 1983

After every decennial census in the U.S., national surveys such as the Consumer Expenditures surveys are redesigned. The redesigned samples will be multi-stage systematic samples. Many sampling schemes have been proposed for comparison which requires the estimation and variance estiamtion formula. This paper deals with the surveys redesign research which concerns the sample design within the Primary Sampling Unit (PSU). In constructing the estimators it deals with the problem of which first stage inflation factor to use. The expected value of the proposed estimators is also derived.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.347-353
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• 2022

In the restaurant industry, start-ups are active due to high demand from consumers and low entry barriers. However, the restaurant industry has a high closure rate, and in the case of franchises, there is a large deviation in sales within the same brand. Thus, research is needed to prevent the closure of food franchises. Therefore, this study examines the factors affecting franchise sales and uses machine learning techniques to predict the success and failure of franchises. Various factors that affect franchise sales are extracted by using Point of Sale (PoS) data of food franchise and public data in Gangnam-gu, Seoul. And for more valid variable selection, multicollinearity is removed by using Variance Inflation Factor (VIF). Finally, classification models are used to predict the success and failure of food franchise stores. Through this method, we propose success and failure prediction model for food franchise stores with the accuracy of 0.92.

• Journal of Navigation and Port Research
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• v.41 no.4
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• pp.181-188
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• 2017

In the existing ship sales market, prices determined based on the prices of similar ship types that recently traded. ince the 2008 financial crisis, ship prices have fluctuated, and ship price criteria have become ever more necessary to the imminent value of the ship. Therefore, this research used the hedonic price model to estimate imminent values of ships. In this study, the influence on ship prices was analyzed by the value of each characteristic and an estimated functional formula was. Out of the four models suggested by the hedonic price model, an optimal model was selected with variance inflation factors and a stepwise selection. For this, the influence of determinants of ship prices was analyzed based on actually traded ships and characteristic data. The selected model s the Log-Line model; as a result of regression analysis, eight variables, including DWT, Age, Market Value, Short-Term Charter, Long-Term Charter, Enbloc, Special Survey Due and Builder were to affect the ship price model. This model is expected to be useful for objective and balanced ship price evaluation.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.04a
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• pp.373-376
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• 2000

We investigate some properties of commonly used residual plots in linear regression and provide some systematic insight into the relationships among the plots. We discuss three issues of linear regression in this stream of context. First of all, we introduce two graphical comparison methods to display the variance inflation factor. Secondly, we show that the role of a suppressor variable in linear regression can be checked graphically. Finally, we show that several other types of standardized regression coefficients, besides the ordinary one, can be obtained in residual plots and the correlation coefficients of one of these residual plots can be used in ranking the relative importance of variables.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.37-46
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• 2000

We investigate some properties of commonly used residual plots in linear regression and provide some systematic insight into the relationships among the plots. We discuss three issues of linear regression in this stream of context. First of all we introduce two graphical comparison methods to display the variance inflation factor. Secondly we show that the role of a suppressor variable in linear regression can be checked graphiclly. Finally we show that several other types of standardized regression coefficients besides the ordinary one can be obtained in residual plots and the correlation coefficients of one of these residual plots can be used in ranking the relative importance of variables.

• The Journal of the Korea Contents Association
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• v.14 no.5
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• pp.524-540
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• 2014

The purpose of this study was to analyze effect young children's temperament and teacher-child relationship on child's leadership. Subject were 3~5 years old 333 kindergarten young children. young children's parents answered young children's temperament questionnaire. young children's teacher answered young children's temperament and teacher-child relationship questionnaire. The research tools used in this study were the questionnaire for assessment scale of young children's temperament, teacher-child relationship and young children's leadership. To analyze effect young children's temperament and teacher-child relationship on young children's leadership, questionnaires were reconstruted from existing questionnaires. Using SPSS statistics 20 for window program, Scheff$\acute{e}$ verification, pearson product moment correlation, mutiple regression analysis, tolerance, variance inflation factor and VIF were used to analyze the data. Results of this study are summarized as follows: In child's temperament, regularity, in teacher-child relationship, closerelationship, in young children's leadership, goal achivement competence were appeared most high. In young children's temperament, adaptabilituy and durability effected on child's leadership positively. In teacher-child relationship, closerelationship and dependency relationship effected on young children's leadership positively.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.77-85
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• 2015

The Hedonic Pricing model is the predominant approach used today to model the effect of relevant factors on real estate prices. These factors include intrinsic elements of a property such as floor areas, number of rooms, and parking spaces. Also, The model also accounts for the impact of amenities or undesirable facilities of a property's value. In the latter case, euclidean distances are typically used as the parameter to represent the proximity and its impact on prices. However, in situations where multiple facilities exist, multi-colinearity may exist between these parameters, which can result in multi-regression models with erroneous coefficients. This research uses Variance Inflation Factors(VIF) and Ridge Regression to identify these errors and thus create more accurate and stable models. The techniques were applied to apartments in Guro-gu of Seoul, whose prices are impacted by subway stations as well as a public prison, a railway terminal and a digital complex. The VIF identified colinearity between variables representing the terminal and the digital complex as well as the latitudinal coordinates. The ridge regression showed the need to remove two of these variables. The case study demonstrated that the application of these techniques were critical in developing accurate and robust Hedonic Pricing models.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.463-473
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• 2018

Variable selection algorithms for linear regression models of large data are considered. Many algorithms are proposed focusing on the speed and the robustness of algorithms. Among them variance inflation factor (VIF) regression is fast and accurate due to the use of a streamwise regression approach. But a VIF regression is susceptible to outliers because it estimates a model by a least-square method. A robust criterion using a weighted estimator has been proposed for the robustness of algorithm; in addition, a robust VIF regression has also been proposed for the same purpose. In this article a fast and robust variable selection method is suggested via a VIF regression with detecting and removing potential outliers. A simulation study and an analysis of a dataset are conducted to compare the suggested method with other methods.