• Analytical Science and Technology
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• v.34 no.6
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• pp.241-250
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• 2021

Calibration curves are essential in quantitative methods and for improving the accuracy of analyte measurements in biological samples. In this study, a statistical analysis model built in the R language (The R Foundation for Statistical Computing) was used to identify a set of weighting factors and regression models based on a stepwise selection criteria. An LC-MS/MS method was used to detect the presence of urinary methamphetamine, amphetamine, and 11-nor-9-carboxy-Δ⁹ -tetrahydrocannabinol in a sample set. Weighting factors for the calibration curves were derived by calculating the heteroscedasticity of the measurements, where the presence of heteroscedasticity was determined via variance tests. The optimal regression model and weighting factor were chosen according to the sum of the absolute percentage relative error. Subsequently, the order of the regression model was calculated using a partial variance test. The proposed statistical analysis tool facilitated selection of the optimal calibration model and detection of methamphetamine, amphetamine, and 11-nor-9-carboxy-Δ⁹-tetrahydrocannabinol in urine. Thus, this study for the selection of weighting and the use of a complex regression equation may provide insights for linear and quadratic regressions in analytical and bioanalytical measurements.

• Near Infrared Analysis
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• v.2 no.1
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• pp.55-58
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• 2001

The object of this work was to investigate the influence of growing district and harvest year on calibration for sweetness (Brix) determination of Fuji apple fruit using near infrared (NIR) reflectance spectroscopy, and to develop the robust calibration across these variation. The calibration models was based on wavelength range of 1100∼2500 nm using a stepwise multiple linear regression. A calibration model by sample set of one growing district was not transferable to other growing districts. The combined calibration (data of three growing districts) predicted reasonable well against a population set drawn from all growing districts (SEP=0.69, Bias=0.075). A calibration model by sample set of one harvest year was not also transferable to other harvest years. The combined calibration (data of three harvest years) predicted well against a population set drawn from all harvest years (SEP=0.53, Bias=0.004).

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.5
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• pp.580-585
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• 1998

The study was conducted to determine if variation in protein yield can be explained by expressions of early lactation somatic cell score (SCS) and if prediction can be improved by including SCS among the predictors. A data set was prepared (n = 663,438) from Wisconsin Dairy Improvement Association (USA) records for protein yield with sample days near 20. Stepwise regression was used requiring F statistic (p < .01) for any variable to stay in the model. Separate analyses were run for 12 combinations of four seasons and first three parities. Selection of SCS variables was not consistent across seasons or lactations. Coefficients of detennination ($R^2$) ranged from 51 to 61% with higher values for earlier lactations. Including any expression of SCS in the prediction equations improved $R^2$ by < 1 %. SCS was associated with milk yield on the sample day, but the association was not strong enough to improve the prediction of future yield when other expressions of milk yield were in the model.

• Bulletin of the Korean Chemical Society
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• v.29 no.10
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• pp.2009-2016
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• 2008

The relationship between thermal decomposition temperature and structure of a new data set of eighty monomers of different polymers were studied by multiple linear regression (MLR). The stepwise method was used in order to variable selection. The best descriptors were selected from over 1400 descriptors including; topological, geometrical, electronic and hybrid descriptors. The effect of number of descriptors on the correlation coefficient (R) and F-ratio were considered. Two models were suggested, one model having four descriptors ($R^2$ = 0.894, $Q^2_{cv}$ = 0.900, F = 172.1) and other model involving 13 descriptors ($R^2$ = 0.956, $Q^2_{cv}$ = 0.956, F = 125.4).

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.894-899
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• 2000

Optimization of 26 design variables selected from suspension characteristics for Korean High Speed Train (KHST) is performed according to the minimization of 58 responses which represent running safety and ride comfort for KHST and analyzed by using the each response surface model from stochastic design experiments. Sensitivity of design variables is also analyzed through the response surface model which ineffective design prameters to the performance index are screened by using stepwise regression method. The response surface models are used for optimizing design variables through simplex algorism. Values of performance index simulated by optimized design parameters are totally lower than those by initial design parameters. It shows that this method is effective for optimizing multi-design variables to multi-object function.

• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.146-150
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• 2021

Plasma information based virtual metrology (PI-VM) that predicts wafer-to-wafer etch rate variation after wet cleaning of plasma facing parts was developed. As input parameters, plasma information (PI) variables such as electron temperature, fluorine density and hydrogen density were extracted from optical emission spectroscopy (OES) data for etch plasma. The PI-VM model was trained by stepwise variable selection method and multi-linear regression method. The expected etch rate by PI-VM showed high correlation coefficient with measured etch rate from SEM image analysis. The PI-VM model revealed that the root cause of etch rate variation after the wet cleaning was desorption of hydrogen from the cleaned parts as hydrogen combined with fluorine and decreased etchant density and etch rate.

• 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.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.179-186
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• 2022

This study aims to develop a predictive model to suggest a used sales price to sellers and buyers when trading used tablet PCs. For model development, we analyzed the real used tablet PC transaction data and additionally collected detailed product information. We developed several predictive models and selected the best predictive model among them. Specifically, we considered a multiple linear regression model using the used sales price as a dependent variable and other variables in the integrated data as independent variables, a multiple linear regression model including interactions, and the models from stepwise variable selection in each model. The model with the best predictive performance was finally selected through cross-validation. Through this study, we can predict the sales price of used tablet PCs and suggest appropriate used sales prices to sellers and buyers.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5483-5488
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• 2012

Background: Although nicotine dependence plays a role as a main barrier for smoking cessation, there is still a lack of solid evidence on the validity of biomarkers to determine nicotine dependence in clinical settings. This study aimed to investigate whether urinary cotinine levels could reflect the severity of nicotine dependence in active smokers. Materials and Methods: Data regarding general characteristics and smoking status was collected using a self-administered smoking questionnaire. The Fagerstr$\ddot{o}$m test for nicotine dependence (FTND) was used to determine nicotine dependence of the participants, and a total of 381 participants were classified into 3 groups of nicotine dependence: low (n=205, 53.8%), moderate (n=127, 33.3%), and high dependence groups (n=49, 12.9%). Stepwise multiple linear regression model and receiver operating characteristic (ROC) curves analyses were used to determine the validity of urinary cotinine for high nicotine dependence. Results: In correlation analysis, urinary cotinine levels increased with FTND score (r=0.567, P<0.001). ROC curves analysis showed that urinary cotinine levels predicted the high-dependence group with reasonable accuracy (optimal cut-off value=1,000 ng/mL; AUC=0.82; P<0.001; sensitivity=71.4%; specificity=74.4%). In stepwise multiple regression analysis, the total smoking period (${\beta}$=0.042, P=0.001) and urinary cotinine levels (${\beta}$=0.234, P<0.001) were positively associated with nicotine dependence, whereas an inverse association was observed between highest education levels (>16 years) and nicotine dependence (${\beta}$=-0.573, P=0.034). Conclusions: The results of this study support the validity of using urinary cotinine levels for assessment of nicotine dependence in active smokers.

• Korean Journal of Health Education and Promotion
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• v.7 no.1
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• pp.64-71
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• 1990

The purpose of this study is to identify the main factors influencing breast cancer self-examination, a preventive health behavior, thereby increasing self-examination compliance for early detection of the disease. The data on which this study was based were collected from a survey of 601 ladies, aged 20∼59 years and residing in Seoul, employing such mehtods as X²-test, ANOVA, t-test, F-test, Person's Correlation Coefficient and Stepwise Multiple Regression. The resulting conclusions are as follows; 1. Discrepancies in self-examination compliance rate are found in accordance with the differences of general characters of the surveyed persons. For instance, those who are well educated and better off are better compliers than those who are not (p<0.001), and those around whom breast cancer patients are better ones than who are not (p<0.01). 2. Self-examination compliers have higher health belief than non-compliers. Compliers have more knowledge in health and have higher susceptibility, barriers and health concern (p<0.001), and higher benefits (p<0.01), and higher seriousness (p<0.05) than non-compliers. 3. Whereas those who have loftier health belief show higher compliance rate (p<0.001), seriousness turned out to have no correlationship with self-examination compliance. 4. Stepwise Multiple Regression portray that following factors influence self-examination compliance in arder named. (1) barriers (2) susceptibility, (3) health concern, (4) age, (5) benefits, (6) education level. Even so, it turned out that these factors alone can explain only 20％ of self-examination compliance. Therefore study for the other factors ought to be continued. I submit following suggestions ending this study. 1. Since breast cancer self-examination is an essential health behavior needed for early detection of the disease, efficient and proper health education program eyed for regular and periodic self-examination is required to be developed, thus reducing the deaths and pains caused by the disease. 2. Proper policies of the government for the prevention of breast cancer is strongly urged to be formed in concrete manner. 3. Continuous study of the other factors affecting self-examination compliance must be carried on.