• Title, Summary, Keyword: Regression Analysis

Search Result 18,892, Processing Time 0.112 seconds

ALC(Autoclaved Lightweight Concrete) Hardness Prediction Research By Multiple Regression Analysis (다중회귀분석을 이용한 ALC 경도예측에 관한 연구)

  • Kim, Gwang-Su;Baek, Seung-Hun
    • Proceedings of the Safety Management and Science Conference
    • /
    • /
    • pp.117-137
    • /
    • 2012
  • In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

  • PDF

Understanding of the Misuse Cases of Quantitative and Qualitative Regression Analysis (정량적, 정성적 회귀분석의 오적용과 이해)

  • Choe, Seong-Un
    • Proceedings of the Safety Management and Science Conference
    • /
    • /
    • pp.213-217
    • /
    • 2011
  • The research shows misuse cases of quantitative regression analysis used in QC circle activity and six sigma movement which presents guidelines of correct use for quality practitioners. Additionally, the qualitative regression analysis that responses nonconforming ratio of variable y, is reviewed based on misuse cases for proper use by practitioners in the field. In most cases, there are frequent errors that involve the correlation analysis or ANOVA, regardless of using quantitative regression analysis. In addition, qualitative regression analysis for the nonconforming ratio that has dependent variable of discrete and categorical data, is often applied with quantitative regression and result in ineffective quality improvement.

  • PDF

Canonical Correlation: Permutation Tests and Regression

  • Yoo, Jae-Keun;Kim, Hee-Youn;Um, Hye-Yeon
    • Communications for Statistical Applications and Methods
    • /
    • v.19 no.3
    • /
    • pp.471-478
    • /
    • 2012
  • In this paper, we present a permutation test to select the number of pairs of canonical variates in canonical correlation analysis. The existing chi-squared test is known to be limited to normality in use. We compare the existing test with the proposed permutation test and study their asymptotic behaviors through numerical studies. In addition, we connect canonical correlation analysis to regression and we we show that certain inferences in regression can be done through canonical correlation analysis. A regression analysis of real data through canonical correlation analysis is illustrated.

Outlier Identification in Regression Analysis using Projection Pursuit

  • Kim, Hyojung;Park, Chongsun
    • Communications for Statistical Applications and Methods
    • /
    • v.7 no.3
    • /
    • pp.633-641
    • /
    • 2000
  • In this paper, we propose a method to identify multiple outliers in regression analysis with only assumption of smoothness on the regression function. Our method uses single-linkage clustering algorithm and Projection Pursuit Regression (PPR). It was compared with existing methods using several simulated and real examples and turned out to be very useful in regression problem with the regression function which is far from linear.

  • PDF

Multivariate Analysis for Clinicians (임상의를 위한 다변량 분석의 실제)

  • Oh, Joo Han;Chung, Seok Won
    • Clinics in Shoulder and Elbow
    • /
    • v.16 no.1
    • /
    • pp.63-72
    • /
    • 2013
  • In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

Switching Regression Analysis via Fuzzy LS-SVM

  • Hwang, Chang-Ha
    • Journal of the Korean Data and Information Science Society
    • /
    • v.17 no.2
    • /
    • pp.609-617
    • /
    • 2006
  • A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

  • PDF

Classification Using Sliced Inverse Regression and Sliced Average Variance Estimation

  • Lee, Hakbae
    • Communications for Statistical Applications and Methods
    • /
    • v.11 no.2
    • /
    • pp.275-285
    • /
    • 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.

A Study for Predicting Building Energy Use with Regression Analysis (회귀분석에 의한 건물에너지 사용량 예측기법에 관한 연구)

  • 이승복
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
    • /
    • v.12 no.12
    • /
    • pp.1090-1097
    • /
    • 2000
  • Predicting building energy use can be useful to evaluate its energy performance. This study proposed empirical approach for predicting building energy use with regression analysis. For the empirical analysis, simple regression models were developed based on the historical energy consumption data as a function of daily outside temperature, the predicting equations were derived for different operational modes and day types, then the equations were applied for predicting energy use in a building. BY selecting a real building as a case study, the feasibilities of the empirical approach for predicting building energy use were examined. The results showed that empirical approach with regression analysis was fairly reliable by demonstrating prediction accuracy of $pm10%$ compared with the actual energy consumption data. It was also verified that the prediction by regression models could be simple and fairly accurate. Thus, it is anticipated that the empirical approach will be useful and reliable tool for many purposes: retrofit savings analysis by estimating energy usage in an existing building or the diagnosis of the building operational problems with real time analysis.

  • PDF

Injection mold Design Optimization using Regression Analysis (회귀분석을 이용한 사출금형 설계 최적화)

  • Ryu M.R.;Kim Y.H.;Lee S.J.;Lee K.H.;Park H.S.
    • Proceedings of the Korean Society of Precision Engineering Conference
    • /
    • /
    • pp.657-660
    • /
    • 2005
  • It is net easy to predict the shrinkage rate of a plastic injection mold in its design process. The shrinkage rate should be considered as one of the important performances to produce the reliable products. The shrinkage rate can be determined by suing the CAE tools in the design produces. However, since the analysis can take minutes to hours, the high computational costs of performing the analysis limit their use in design optimization. Therefore this study was carried out to presume for mutual relation of analysis condition to get the optimum average shrinkage by regression analysis. The results shown that coefficient of determination of regression equation has a fine reliability over 88.3% and regression equation of average shrinkage is made by regression analysis.

  • PDF

ALC(Autoclaved Lightweight Concrete) Hardness Prediction by Multiple Regression Analysis (다중회귀분석을 이용한 ALC 경도예측에 관한 연구)

  • Kim, Kwang-Soo;Baek, Seung-Hoon;Chung, Soon-Suk
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
    • /
    • v.7 no.2
    • /
    • pp.101-111
    • /
    • 2012
  • In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

  • PDF