• 제목/요약/키워드: Variance of Analysis

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A Study on the design of hospital budget variance analysis model reflecting efficiency and an attainable target cost (효율성과 목표원가를 반영한 병원예산 원가차이 분석 모형 설계)

  • O, Dongil
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.2
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    • pp.696-706
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    • 2013
  • This study aims to develop DEA model which can look into budget cost variance analysis tool based on standard cost using 68 hospital's input and output data. For accomplishing this purpose, by introducing new DEA model which can get an attainable target cost, we can decompose an actual cost difference into several meaningful sub variances. Also based on the 2008 general hospital data, this model can make variance analysis between actual cost and target cost. Total variance can be divided into technical inefficiency variance, price inefficiency variance, allocation inefficiency variance. This study introduces that by using target budget cost concept, traditional actual cost variance can be divided into a technical variance, price variance, budget variance. Finally, we can get result which confirms there does not exist favorable size effects on efficiency and cost management in running a general hospital.

Evaluation of Panel Performance by Analysis of Variance, Correlation Analysis and Principal Component Analysis (패널요원 수행능력 평가에 사용된 분산분석, 상관분석, 주성분분석 결과의 비교)

  • Kim, Sang-Sook;Hong, Sung-Hie;Min, Bong-Kee;Shin, Myung-Gon
    • Korean Journal of Food Science and Technology
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    • v.26 no.1
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    • pp.57-61
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    • 1994
  • Performance of panelists trained for cooked rice quality was evaluated using analysis of variance, correlation analysis, and principal component analysis. Each method offered different information. Results showed that panleists with high F ratios (p=0.05) did not always have high correlation coefficient (p=0.05) with mean values pooled from whole panel. The results of analysis of variance for the panelists whose performance were extremely good or extremely poor were consistent with those of correlation analysis. Outliers designated by principal component analysis were different from the panelists whose performance was defined as extremely good or extremely poor by analysis of variance and correlation analysis. The results of principal component analysis descriminated the panelists with different scoring range more than different scoring trends depending on the treatments. Our study suggested combination of analysis of variance and correlation analysis provided valid basis for screening panelists.

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A Study on the Methods of the Overhead Standard Setting and the Overhead Variance Analysis in Standard Cost Accounting (표준원가계산에 있어서 제조간접비표준의 설정과 차이분석기법)

  • 김선정
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.6 no.8
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    • pp.81-91
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    • 1983
  • In this study the methods of the overhead standard setting and the overhead variance analysis, which raise problems especially in business practice in case that small businesses introduce the standard cost accounting system, were examined by hypothetical examples. As the result of this study small businesses are advised to take the following in setting the overhead cost. (1) To divide the mixed cost into variable overhead and fixed overhead, it is desirable to take Beast square method. (2) In setting the overhead standard, it is desirable to fake the flexible budget system and to make a budget by the inspection method, after dividing the overhead into variable overhead and fixed overhead. (3) After dividing the overhead variance into variable overhead variance and fixed overhead variance, it is desirable to analyze them as follows. (A) Variable overhead variance is analyzed into spending variance and efficiency variance. (B) Fixed overhead valiance is analyzed into budget variance and denominator variance.

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Classification Using Sliced Inverse Regression and Sliced Average Variance Estimation

  • Lee, Hakbae
    • Communications for Statistical Applications and Methods
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    • v.11 no.2
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    • pp.275-285
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    • 2004
  • We explore classification analysis using graphical methods such as sliced inverse regression and sliced average variance estimation based on dimension reduction. Some useful information about classification analysis are obtained by sliced inverse regression and sliced average variance estimation through dimension reduction. Two examples are illustrated, and classification rates by sliced inverse regression and sliced average variance estimation are compared with those by discriminant analysis and logistic regression.

An Analysis of Variance Procedure for the Split-Plot Design Using SPSS Syntax Window

  • Choi Byoung-Chul
    • Communications for Statistical Applications and Methods
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    • v.12 no.1
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    • pp.61-69
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    • 2005
  • In conducting the analysis of variance for the split-plot design using the statistical package SPSS, users including statisticians are faced with difficulties because of no appropriate example in the SPSS applications guide book. In this paper, therefore, we present an analysis of variance procedure for the split-plot design using SPSS syntax window.

Evaluation Method for Measurement System and Process Capability Using Gage R&R and Performance Indices (게이지 R&R과 성능지수를 이용한 측정시스템과 공정능력 평가 방법)

  • Ju, Youngdon;Lee, Dongju
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.42 no.2
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    • pp.78-85
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    • 2019
  • High variance observed in the measurement system can cause high process variation that can affect process capability badly. Therefore, measurement system analysis is closely related to process capability analysis. Generally, the evaluation for measurement system and process variance is performed separately in the industry. That is, the measurement system analysis is implemented before process monitoring, process capability and process performance analysis even though these analyses are closely related. This paper presents the effective concurrent evaluation procedure for measurement system analysis and process capability analysis using the table that contains Process Performance (Pp), Gage Repeatability & Reproducibility (%R&R) and Number of Distinct Categories (NDC). Furthermore, the long-term process capability index (Pp), which takes into account both gage variance and process variance, is used instead of the short-term process capability (Cp) considering only process variance. The long-term capability index can reflect well the relationship between the measurement system and process capability. The quality measurement and improvement guidelines by region scale are also described in detail. In conclusion, this research proposes the procedure that can execute the measurement system analysis and process capability analysis at the same time. The proposed procedure can contribute to reduction of the measurement staff's effort and to improvement of accurate evaluation.

ON THE ADMISSIBILITY OF HIERARCHICAL BAYES ESTIMATORS

  • Kim Byung-Hwee;Chang In-Hong
    • Journal of the Korean Statistical Society
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    • v.35 no.3
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    • pp.317-329
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    • 2006
  • In the problem of estimating the error variance in the balanced fixed- effects one-way analysis of variance (ANOVA) model, Ghosh (1994) proposed hierarchical Bayes estimators and raised a conjecture for which all of his hierarchical Bayes estimators are admissible. In this paper we prove this conjecture is true by representing one-way ANOVA model to the distributional form of a multiparameter exponential family.

NONPARAMETRIC ESTIMATION OF THE VARIANCE FUNCTION WITH A CHANGE POINT

  • Kang Kee-Hoon;Huh Jib
    • Journal of the Korean Statistical Society
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    • v.35 no.1
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    • pp.1-23
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    • 2006
  • In this paper we consider an estimation of the discontinuous variance function in nonparametric heteroscedastic random design regression model. We first propose estimators of the change point in the variance function and then construct an estimator of the entire variance function. We examine the rates of convergence of these estimators and give results for their asymptotics. Numerical work reveals that using the proposed change point analysis in the variance function estimation is quite effective.

VARIANCE ESTIMATION OF ERROR IN THE REGRESSION MODEL AT A POINT

  • Oh, Jong-Chul
    • Journal of applied mathematics & informatics
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    • v.13 no.1_2
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    • pp.501-508
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    • 2003
  • Although the estimate of regression function is important, some have focused the variance estimation of error term in regression model. Different variance estimators perform well under different conditions. In many practical situations, it is rather hard to assess which conditions are approximately satisfied so as to identify the best variance estimator for the given data. In this article, we suggest SHM estimator compared to LS estimator, which is common estimator using in parametric multiple regression analysis. Moreover, a combined estimator of variance, VEM, is suggested. In the simulation study it is shown that VEM performs well in practice.