Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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초등학교 교원 수 예측을 위한 시계열 회귀모형

Time series regression model for forecasting the number of elementary school teachers

  • 류수락 (대구대학교 전산통계학과) ;
  • 김종태 (대구대학교 전산통계학과)
  • Ryu, Soo Rack (Department Computing.Statistics, Daegu University) ;
  • Kim, Jong Tae (Department Computing.Statistics, Daegu University)
  • 투고 : 2013.02.01
  • 심사 : 2013.03.14
  • 발행 : 2013.03.31
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초록

본 연구는 지속적인 저출산의 여파로 2020년에는 초등학생 수가 올해 대비 17%, 중고교생은 30%가 감소할 것이라는 예측을 가지고 초등학교 교원 수를 예측하기 위한 방법을 제시하는데 있다. 교육통계연보의 1970년부터 2010년까지의 초등교육 관련 주요 통계 자료를 이용하여 시계열 회귀모형과 시계열 그룹별 회귀모형, 지수평활법 모형을 제시하고, 제시된 모형을 이용하여 향후 10년간의 연도별 초등학교 교원 수를 예측하였다. 모형 예측 결과 시계열 그룹별 회귀 모형이 교원 수 시계열을 가장 잘 설명하는 것으로 나타났으며, 적합한 모형으로 판명되었다. 3가지 분석방법 모형에 따른 예측값에 대한 장단점과 한계를 제시한다.

Because of the continuous low birthrates, the number of the elementary students will decrease by 17% in 2020 compared to 2011. The purpose of this study is to forecast the number of elementary school teachers until 2020. We used the data in education statistical year books from 1970 to 2010. We used the time-series regression model, time series grouped regression model and exponential smoothing model to predict the number of teachers for the next ten years. Consequently time-series grouped regression model is a better model for forecasting the number of elementary school teachers than other models.

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참고문헌

  1. Kim, H. C. (2000). Mid and long term forecasts of the number of graduates of universities of education and the number of newly recruited teachers. The Joumal of Korean Education Administration, 18, 133-149.
  2. Kim, H. C. (2002). Forecasting of the number of the elementary school teachers using time-series data analysis：A search for the explanatory variables and the comparison of the results from different forecasting methods. The Joumal of Korean Education, 29, 113-130.
  3. Kim, J. T. (2000a). The forecasting about the numbers of the third graders in a high-school until 2022 year in Daegu. Journal of the Korean Data & Information Science Society, 16, 933-942.
  4. Kim, J. T. (2005b). The forecasting for the numbers of a high-school graduate and the number limit of matriculation in Kyungbook. Journal of the Korean Data & Information Science Society, 16, 969-977.
  5. Korean Educational Development Institute (1970-2010). Education statistical year book, Educational Statistics & Information, Seoul.
  6. OECD Educational Information Center. (2010). 2010 OECD education index, OECD Report, Seoul.
  7. Statistics Korea (2010). Population projections, Korean Statistical Information Service, Daejeon.
  8. Yoon, Y. H. and Kim, J. T. (2012). Estimations of the student numbers by nonlinear regression model. Journal of the Korean Data & Information Science Society, 23, 71-77. https://doi.org/10.7465/jkdi.2012.23.1.071

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