• Journal of Preventive Medicine and Public Health
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• v.23 no.4 s.32
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• pp.436-450
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• 1990

This study was designed to investigate the mar factors affecting selection of delivery facilities by pregnant women. Five hundred women hospitalized at 23 Seoul-area delivery facilities, such as university hospitals, general hospitals, hospitals, and clinics were selected and given questionnaires from April 24 to May 7, 1990. A total of 350 questionnaires were collected and analysed for the study. The results are as follows ; 1. In general, variables which significantly affected the choice of delivery facilities included the age of women, their educational level, the educational level of their husbands, monthly average incomes and residential areas. 2. In analyzing the obstetrical characteristics of the women, those variables significantly affecting the choice of delivery facilities were the gestational period, the facilities for prenatal care, the frequency of prenatal care, the type of delivery, the frequency of miscarriage, previous delivery experiences and the awareness on prenatal care. 3. In comparing the motivation factors for selecting the delivery facilities, all the factors except convenience and need for hospitalization differed significantly among delivery facilities. 4. The factor analysis was assessed for twenty possible factors motivating the choice of delivery facilities. Six factors including personal service, scale of the facility, reputation, urgency, convenience, and experience were noted explaining by 57.7%. 5. In the discriminant analysis used to clarify the major factors affecting the selection of delivery facilities, the 16 significant variables were regarded as independent variables, and the type of delivery facilities was considered a dependent variable. The stepwise method was applied to the analysis. Detected discriminant variables were the facilities for prenatal care, scale factor, personal service factor, urgency factor, convenience factor, reputation factor, experience factor, gestational period, types of delivery, frequency of miscarriage, age and income. These 12 discriminant variables were tested, with reference to discriminant prediction, on their importance in the choice of the delivery facility, by the discriminant functional formula. The test showed a hit-rate of 67.7%. The results suggest that general characteristics, obstetrical characteristics, and motivations for selecting the delivery facilities differ significantly according to the types of the delivery facilities. This study implies that all types of delivery facilities should attempt to acommodate characteristics and motivations of pregnant women. The facilities should be prepared to increase their patients satisfaction with required medical conditions by improving service and responding to the pregnant women's preferences.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.391-404
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• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Asia Marketing Journal
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• v.10 no.2
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• pp.1-29
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• 2008

The objective of our study is to provide an exploratory model for forecasting sales take-off timing of a product in the context of multi-national markets. We evaluated the usefulness of key predictors such as multiple market information, product attributes, price, and sales for the forecasting of sales take-off timing by applying the suggested model to monthly sales data for PDP and LCD TV provided by a Korean electronics manufacturer. We have found some important results for global companies from the empirical analysis. Firstly, innovation coefficients obtained from sales data of a particular product in other markets can provide the most useful information on sales take-off timing of the product in a target market. However, imitation coefficients obtained from the sales data of a particular product in the target market and other markets are not useful for sales take-off timing of the product in the target market. Secondly, price and product attributes significantly influence on take-off timing. It is noteworthy that the ratio of the price of the target product to the average price of the market is more important than the price ofthe target product itself. Lastly, the cumulative sales of the product are still useful for the prediction of sales take-off timing. Our model outperformed the average model in terms of hit-rate.