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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- 김승규
- 발행연도
- 2014
- 저작권
- 경북대학교 논문은 저작권에 의해 보호받습니다.
이용수15
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초록· 키워드
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Crops and varieties are mostly affected by temperature, amount of precipitation, and duration of sunshine.This study aims to identify the weather factors that directly influence to apple yield among the series of daily measured weather variables during growing seasons. In order to identify them, 1) a priori natural scientific knowledge with respect to the growth stage of apples and 2) pure statistical approaches to minimize bias due to the subject selection of variables are considered. Pooled ordinary least squares(OLS) and panel analysis of fixed/random effect models are used. Each result is evaluated by suitability(i.e., Akaike information criterion and Bayesian information criterion) and predictability(i.e., mean absolute error, root mean square error, mean absolute percentage).
As a case study, panel data of apple yield and weather factors are collected for fifteen major producing areas of apples from 2006 to 2013 in Korea. The result shows that variable selection using factor analysis, which is one of the statistical approach applied in the analysis, increases predictability and suitability most. It may imply that all weather factors are important to predict apple yield if statistical problems such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression can be controlled effectively. This may be because whole growth stages such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting are all affected by weather variables.
As a case study, panel data of apple yield and weather factors are collected for fifteen major producing areas of apples from 2006 to 2013 in Korea. The result shows that variable selection using factor analysis, which is one of the statistical approach applied in the analysis, increases predictability and suitability most. It may imply that all weather factors are important to predict apple yield if statistical problems such as multicollinearity and lower degree of freedom due to too many explanatory variables used in the regression can be controlled effectively. This may be because whole growth stages such as germination, florescence, fruit setting, fatting, ripening, coloring, and harvesting are all affected by weather variables.
목차
Ⅰ. 서론 11. 연구의 필요성 12. 연구의 배경 33. 선행연구 검토 74. 연구의 목적 10Ⅱ. 분석모형 121. 패널(Panel)데이터의 개념 및 특성 122. 합동(Pooled) OLS 분석 133. 패널(Panel) 데이터 분석 134. 모형 적합도 및 예측력 평가 17Ⅲ. 분석자료 201. 분석대상 및 데이터 202. 변수설정 26Ⅳ. 실증분석 결과 341. 실증분석 342. 모형추정결과 363. 모형의 적합도 및 예측력 비교결과 46Ⅴ. 요약 및 결론 48참고문헌 52부 록 55Abstract 60
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