• Journal of Korean Society of Water and Wastewater
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• v.25 no.6
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• pp.877-884
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• 2011

Although peak loading coefficient is one of critical design factors for sewer works, its detailed affecting factors were not analyzed because of limited data availability. This study analyzed the affecting factors on peak loading coefficient with plenty data obtained from several newly constructed sewer works. Simple and multiple regression analysis methods were adopted to analyze the relationships of each variable with or without data filtering. Drainage population, drainage area, population density, and daily sewage flow per person showed very weak relationships under diverse characteristics of cities. However, daily sewage flow per person showed stronger relationships with peak loading when daily sewage flow per person was splitted into two ranges. Population density (i.e., drainage population divided by drainage area) and daily sewage flow per person considerably were related with peak loading coefficient when daily sewage flow per person is less than about 400 Lpcd.

• Asian-Australasian Journal of Animal Sciences
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• v.9 no.6
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• pp.723-726
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• 1996

A water loaded pressure device using water as the breaking force was developed to evaluate eggshell strength and compared with a dropping ball techniques. Further, relationships of shell thickness and weight of eggs to shell strength were also studied. Values for both of the shell strength measuring methods showed a highly significant correlation (p < 0.001) with shell thickness. The water loaded pressure method had a much higher simple correlation coefficient for shell thickness (r = + 0.786) than the dropping ball method (r = + 0.577). The shell strength measured by the water loaded pressure method appeared not to be correlated to egg weight. On the other hand, the negative sign of the standard partial regression coefficient and the partial regression coefficient of egg weight in the estimated multiple regression equation implied that for a given shell thickness a larger egg tended to have less shell strength than a smaller egg.

• Journal of Veterinary Clinics
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• v.27 no.4
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.32-47
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• 1995

The coefficient of determination R/sup 2/, as the proprtation of by explained by a set of independent variavles x/sub 1/, x/sub 2, .cdots., x/sub k/ through a linear regression model, is a very useful tool in linear regression analysis. Suppose R/sup 2//sub yx/ is the coefficient of determination when y is regressed only on x/sub i/ alone. If the independent variables are correlaated, the sum, R/sup 2//sub {yx/sub 1/}/ +R/sup 2//sub {yx/sub 2/}/+.cdots.R/sup 2//sub {yx/sub k/}/, is not equal to R/sup 2/sub {yx/sub 1/x/sub 2/.cots.x/sub k/}/, where R/sup 2//sub {yx/sub 1/x/sub 2/.cdots.x/sub k/}/ is the coefficient of determination when y is regressed simultaneously on x/sub 1/, x/sub 2/,.cdots., x/sub k/. In this paper it is discussed that under what conditions the sum is greater than, equal to, or less than R/sup 2//sub {yx/sub 1/x/sub 2/.cdots.x/sub k/}/, and then the proofs for these conditions are given. Also illustrated examples are provided. In addition, we will discuss about inequality between R/sup 2//sub {yx/sub 1/x/sub 2/.cdots.x/sub k/}/ and the sum, R/sup 2//sub {yx/sub 1/}/+R/sup 2//sub {yx/sub 2/}/+.cdots.+R/sup 2//sub {yx/sub k/}/.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.1334-1339
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• 2002

The objective of this study is to help practicing engineers easily use the Clark model which is used for estimating the magnitude of design flood for small stream. A representative unit hydrograph was derived on the basis of the past rainfall-runoff data and unit hydrographs, and the storage coefficient of Clark model was estimated by using hydrograph recession analysis. Since the storage coefficient(K) is a dominating factor among the parameters of Clark method, a mulitple regression formula, which has the drainage area, main channel length and slope as parameters, is propsed to estimate K value of a basin where measured data are missing. The result of regression analysis showed that there is a correlation between a storage coefficient(K) and aforemetioned three parameters in homogenious basins. A regression formular for K was derived using these correlations in a basin of Han River, Nakdong River, Young River, Kum River and Sumjin River

• The Journal of Engineering Geology
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• v.20 no.1
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• pp.35-45
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• 2010

The soil tests have been performed on the specimens obtained from about 1,150 sites including landslides and non-landslides areas in natural terrains for last 10 years. Based on the results of those tests, the average soil properties are estimated and the simple equations for estimating permeability are proposed according to geologic conditions. The average permeability in Granite and Mudstone sites is higher than other sites and the content of silt and clay in Mudstone and Gneiss sites is higher than other sites. The correlation analysis and the regression analysis were performed to estimate the coefficient of permeability according to geological conditions. As the result of the correlation analysis, the coefficient of permeability is selected as a dependent variable, and the silt and clay contents, the water contents and the dry unit weights are selected as independent variables. As the result of the regression analysis, the silt and clay contents and the void ratio were involved commonly in the linear regression equations according to geological conditions. To verify the proposed the linear regression equations, the measured result of the coefficient of permeability at other sites was compared with the result predicted with the proposed equations. As the result of comparison, there were a little bit different between them for some data. However the difference was relatively small. Therefore, the linear regression equations for estimating the coefficient of permeability according to geological conditions may be applied to Korean soils. However, these equations should be verified and corrected continuously to improve the accuracy.

• Journal of Microbiology and Biotechnology
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• v.10 no.4
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• pp.476-481
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• 2000

A linear correlation, by the stepwise multiple regression analysis, was found between the sensory test of Sigumjang aroma and the gas chromatographic data which were transformed with logarithm. GC data is the most objective method to evaluate Sigumjang aroma. A multiple correlation coefficient and a determination coefficient of more than 0.9 were obtained at the 9th and 13th steps, respectively. At step 31, the coefficient of determination level of 0.95 was attained. The accuracy of its estimation became higher as the number of the variables entered into the regression model increased. Over 90% of the Sigumjang aroma was explained by 13 compounds indentified on GC. The contributing proportion of the peak 26 was the highest followed by peaks 57 (9.27%), 29 (7.51%), 54 (6.01%), 8 (5.99%), 49 (4.97%), and 13 (4.11%).

• Proceedings of the Culinary Society of Korean Academy Conference
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• 2006.08a
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• pp.115-134
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• 2006

This study was conducted during a period from July 4 to 30 to investigate the effect of restaurant environment upon customer's satisfaction and crowdedness awareness. Total 800sets of questionnaire were distributed among major food service corporations. They were 16 restaurants from McDonald, Burger King, Popeyes, KFC, Rits Carlton, Intercontinental, The Westin Chosun, Hilton, Merriot, Outback Steak House, Bennigans, VIPS, Pizza Hut Pul-hyanggi(Scent of grass), Nolboo Co.,, and Our Story, and received 50 see each to hand out to their customers. Out of total 800 sets of questionnaires, 592 sets (74.25% were retrieved and underwent a Multiple Regression Analysis. We found the following results from the study. First among each variable of restaurant environment that had a significant effect on the crowding, 'pTast service' and 'responsiveness to customer complaints' sooted a regression coefficient value 0.381 and 0.325 respectively. Second, among each restaurant environment factor that had a significant effect on crowding, 'quality of facility' sooted the highest regression coefficient value 0.423 with a standard error score 0.1074, fellowed by 'status of waiting', 'overall ambience' and 'service quality' in ascending order. Third, in the analysis of the effect of each environmental factor upon the satisfaction rate, 'status of waiting' showed the highest regression coefficient value 0.3821 with a standard error score 0.4565, followed by 'cleanliness', 'service quality' and 'conveniency', in ascending order.

• Culinary science and hospitality research
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• v.12 no.4 s.31
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• pp.63-79
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• 2006

This study was conducted to investigate the effect of restaurant environment upon customers' satisfaction and crowdedness awareness from July 4 to 30. Total 800 sets of questionnaire were distributed among major foodservice corporations including 16 restaurants from McDonald, Burger King, Popeyes, KFC, Ritz Carlton, Intercontinental, The Westin Chosun, Hilton, Merriot, Outback Steak House, Bennigans, VIPS, Pizza Hut, Pul-hyanggi(Scent of grass), Nolboo Co., and Our Story. They received 50 sets each to hand out to their customers. Out of total 800 sets of questionnaires, 592 sets (74.25%) were retrieved and underwent the Multiple Regression Analysis. We found the following results from the study. First, among each variable of restaurant environment that had a significant effect on crowdedness, "fast service" and "responsiveness to customer complaints" scored a regression coefficient value 0.381 and 0.325 respectively. Second, among each restaurant environment factor that had a significant effect on crowdedness, "quality of facilities" scored the highest regression coefficient value 0.423 with a standard error score 0.1074, followed by "condition of waiting", "overall ambience" and "service quality" in ascending order. Third, in the analysis of the effect of each environmental factor upon the satisfaction rate, "condition of waiting" showed the highest regression coefficient value 0.3821 with a standard error score 0.4565, followed by "cleanliness", "service quality" and "convenience', in ascending order.