• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.589-597
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• 2021

In simple and multiple regression, there is a difference in the meaning of regression coefficients, and not only are the estimates of regression coefficients different, but they also have different signs. Understanding the relative contribution of explanatory variables in a regression model is an important part of regression analysis. In a standardized regression model, the regression coefficient can be interpreted as the change in the response variable with respect to the standard deviation when the explanatory variable increases by the standard deviation in a situation where the values of the explanatory variables other than the corresponding explanatory variable are fixed. However, the size of the standardized regression coefficient is not a proper measure of the relative importance of each explanatory variable. In this paper, the estimator of the regression coefficient in multiple regression is expressed as a function of the correlation coefficient and the coefficient of determination. Furthermore, it is considered in terms of the effect of an additional explanatory variable and additional increase in the coefficient of determination. We also explore the relationship between estimates of regression coefficients and correlation coefficients in various plots. These results are specifically applied when there are two explanatory variables.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.505-516
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• 2015

We consider sparse high-dimensional linear regression models. Penalized regressions have been used as effective methods for variable selection and estimation in high-dimensional models. In penalized regressions, it is common practice to standardize variables before fitting a penalized model and then fit a penalized model with standardized variables. Finally, the estimated coefficients from a penalized model are recovered to the scale on original variables. However, these procedures produce a slightly different solution compared to the corresponding original penalized problem. In this paper, we investigate issues on the standardization of variables in penalized regressions and formulate the definition of the standardized penalized estimator. In addition, we compare the original penalized estimator with the standardized penalized estimator through simulation studies and real data analysis.

• Journal of Nutrition and Health
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• v.31 no.3
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• pp.354-362
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• 1998

The purposes of this study were to develop standardized indices of staffing needs in each school, foodservice system through work sampling methodology . Conventional school foodservices were classified into 5 groups depending on size of meals served. Commissary school foodservices were also classified into 5 groups by cluster analysis using number of meals served, number of satellite schools, and time for transportation of food. Work measurement through work sampling methodology was conducted in 15 conventional and 21 commissary foodservices during 3 consecutive days from September to October in 1995. Statistical data analysis was completed using the SAS programs for descriptive analysis, cluster analysis, and simple linear regression. The results were as follows : Average points of leveling factors of conventional and commissary foodservices were 1.066 and 1.061 , respectively. Mean labor hours per work force was 328 minutes and 366 minutes in conventional and commissary foodservice , respectively. Standardized work time was calculated using leveling factor, ILO allowance rate (175) , and observational work time. The model for standardized indices of staffing needs was developed based on simple linear regression in each school foodservice system. In conventional school foodservice systems(for 100-1,900 meals per day) standardized staffing needs=3.2497 +0.005267$\times$number of meals served (F=273.1, R-square 0.9750, p<0.001). In commissary school foodservice systems (for 200-1,600 meals per day ) Standardized staffing needs=3.393384 +0.0063$\times$number of meals served (F=30.78, R-square 0.6580, p<0.001).

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.447-459
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• 2010

The present study was carried out to summarize the effect of propolis in the diabetic rats by meta-analysis related studies. The association measure to test effect of propolis was Hedges's standardized mean difference between group of rats induced streptozotocin(STZ) or alloxan and group of rats induced STZ or alloxan treated with propolis about the considered 4 effect factors. In this particular fixed-effect model, blood glucose, Cholesterol, Triglyceride were significantly reduce. The case of heterogenous variable such as body weight, blood glucose, cholesterol, triglyceride, random-effect model was applied. In this model, blood glucose, triglyceride were decreased significantly in propolis treated group. According to the meta-regression analysis, period of injection was significant for body weight and blood glucose, cholesterol.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.717-726
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• 2011

The present study was carried out to summarize the effect of green tea in the diabetic rats by meta-analysis related studies. The association measure to test effect of green tea was Hedges' standardized mean difference. In this particular fixed effect model, body weight was significantly increased. Also, blood glucose, triglycerides were significantly decreased. In this case of heterogeneous variable, random effect model was applied. In this model, body weight was significantly increased. Also, blood glucose was significantly decreased in green tea treated group. According to the Meta-regression analysis, duration of injection was not significant for variables.

• Fashion & Textile Research Journal
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• v.17 no.2
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• pp.278-286
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• 2015

Fashion industry has tried to adopt the virtual garment technology to reduce the time and effort spent on sample creation. For garment manufacturers to adopt the virtual garment technology as an alternative to sample creation, 3D avatars that meet the needs of each brand should be developed. Virtual garment softwares that are available in the market provide avatars with standardized body models and allow to modify the size by manually entering size specifications. This study proposed a methodology to develop size specifications for 3D avatars as well as brand-customized production sizes. For this, a man's fashion brand which is using virtual garment technology is selected. And the Size Korea database is used to develop size specification based on the customers' body shape. This study developed regression equations on body size specifications, which in turn proposed a regression model to proportionately change size specifications of 3D fitting-models. Based on the each body size calculated by the regression model, a standard model is created, and the skeleton-skin algorithm is applied to the regression model to obtain the results of size changes. Then, the 3D model sizes are tested for size changes as well as measured, which verifies that the regression model reflects body size changes.

• Environmental Engineering Research
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• v.19 no.1
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• pp.31-36
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• 2014

In this study, the influence of the control and operational parameters within a sewage treatment plant were reviewed by performing multiple regression analysis on the effluent quality of the sewage treatment. The data used for this review are based on the actual data from a sewage treatment plant using the media process within the year 2012. The prediction models of chemical oxygen demand ($COD_{Mn}$) and total nitrogen (T-N) within the effluent of the 2nd settling tank based on the multiple regression analysis yielded the prediction accuracy measurements of 0.93 and 0.84, respectively; and it was concluded that the model was accurately predicting the variances of the actual observed values. If the data on the energy spent on each operating condition can be collected, then the operating parameter that conserves energy without violating the effluent quality standards of COD and T-N can be determined using the regression model and the standardized regression coefficients. These results can provide appropriate operation guidelines to conserve energy to the operators at sewage treatment plants that consume a lot of energy.

• Health Policy and Management
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• v.25 no.3
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• pp.197-206
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• 2015

Background: The purpose of this study was to propose a method for developing a measure of hospital-wide all-cause risk-standardized readmissions using administrative claims data in Korea and to discuss further considerations in the refinement and implementation of the readmission measure. Methods: By adapting the methodology of the United States Center for Medicare & Medicaid Services for creating a 30-day readmission measure, we developed a 6-step approach for generating a comparable measure using Korean datasets. Using the 2010 Korean National Health Insurance (NHI) claims data as the development dataset, hierarchical regression models were fitted to calculate a hospital-wide all-cause risk-standardized readmission measure. Six regression models were fitted to calculate the readmission rates of six clinical condition groups, respectively and a single, weighted, overall readmission rate was calculated from the readmission rates of these subgroups. Lastly, the case mix differences among hospitals were risk-adjusted using patient-level comorbidity variables. The model was validated using the 2009 NHI claims data as the validation dataset. Results: The unadjusted, hospital-wide all-cause readmission rate was 13.37%, and the adjusted risk-standardized rate was 10.90%, varying by hospital type. The highest risk-standardized readmission rate was in hospitals (11.43%), followed by general hospitals (9.40%) and tertiary hospitals (7.04%). Conclusion: The newly developed, hospital-wide all-cause readmission measure can be used in quality and performance evaluations of hospitals in Korea. Needed are further methodological refinements of the readmission measures and also strategies to implement the measure as a hospital performance indicator.

• Animal Bioscience
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• v.37 no.6
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• pp.1077-1084
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• 2024

Objective: The present work was conducted to evaluate suitable variables and develop prediction equations using chemical composition and physical characteristics for estimating standardized ileal digestibility (SID) of lysine (Lys) in full-fat soybeans (FFSB). Methods: The chemical composition and physical characteristics were determined including trypsin inhibitor activity (TIA), urease activity (UA), protein solubility in 0.2% potassium hydroxide (KOH), protein dispersibility index (PDI), lysine to crude protein ratio (Lys:CP), reactive Lys:CP ratio, neutral detergent fiber, neutral detergent insoluble nitrogen (NDIN), acid detergent insoluble nitrogen (ADIN), acid detergent fiber, L* (lightness), and a* (redness). Pearson's correlation (r) was computed, and the relationship between variables was determined by linear or quadratic regression. Stepwise multiple regression was performed to develop prediction equations for SID of Lys. Results: Negative correlations (p<0.01) between SID of Lys and protein quality indicators were observed for TIA (r = -0.80), PDI (r = -0.80), and UA (r = -0.76). The SID of Lys also showed a quadratic response (p<0.01) to UA, NDIN, TIA, L*, KOH, a* and Lys:CP. The best-fit model for predicting SID of Lys in FFSB included TIA, UA, NDIN, and ADIN, resulting in the highest coefficient of determination (R² = 0.94). Conclusion: Quadratic regression with one variable indicated the high accuracy for UA, NDIN, TIA, and PDI. The multiple linear regression including TIA, UA, NDIN, and ADIN is an alternative model used to predict SID of Lys in FFSB to improve the accuracy. Therefore, multiple indicators are warranted to assess either insufficient or excessive heat treatment accurately, which can be employed by the feed industry as measures for quality control purposes to predict SID of Lys in FFSB.

• Journal of Nutrition and Health
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• v.39 no.4
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• pp.417-425
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• 2006

The purpose of this study were to a) investigate operation of human resource in contract foodservice management company (CFMC), b) identify the staffing indices for the establishment an labor productivity for CFMC, and c) develop standardized model of staffing demand as foodservice's scale in CFMC. The data was collected using FS intra-net system from 138 contract-managed foodservice operations in A CFMC and statistical analysis was completed using the SAS/win package (ver. 8.0) for description analysis, ANOVA, Duncan multiple comparison, pearson correlation analysis, and regression analysis. The types of operation were included factory (45%), small scale operation (26%), office (11%), department store (10%), training institute (4%), and hospital (3%). The distribution of foodservice scale was classified by meal served was as follows; 'less than 500 meals (47%)', 'from 500 to 1500 meals (25%)', 'from 1500 to 2500 meals (17%)', and 'more than 2500 meals (12%)'. There was two types of contract method, fee-contract (53%) and profit-and-loss contract (46%) Some variables were significantly high operation indices such as selling price, food cost, monthly sales, net profit and others were significantly low operation indices such as labor, meal time a day in the small foodservice on meal scale (p<.001). The more foodservice was large, the more human resource was disposed on dietitian, cook, cooking employee altogether (p<.001). Foodservice in A CFMC was divided into 2 groups by 500 meals a day, according to comparative analysis of labor productivity as meal scale per working hour, meal scale a day and operation indices as meal per foodservice employee, meal per cooking employee (p<.001). The regression equation model was developed as 'the number of employees=1.82+0.014 ${\times}$ meal served' in the operation of less than 500 meals, 'the number of employees=9.42+0.013 ${\times}$ meal scale a day -0.94 ${\times}$ meal scale per working hour' in the operation over 500 meal scale using labor productivity indices and operation indices. Therefore, CFMC could be enhanced efficiency of human resource arrangement using the standardized model of staffing demand and would be increased effectiveness of profit.