응용통계연구 (The Korean Journal of Applied Statistics)

  • 제34권4호
  • /
  • Pages.633-658
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  • 2021
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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통계공학을 위한 Python 패키지 응용

Applications of python package for statistical engineering

  • 투고 : 2021.04.30
  • 심사 : 2021.05.17
  • 발행 : 2021.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

통계공학은 실험계획법, 품질관리/품질경영, 신뢰성공학으로 구성된다. Python은 무료로 개방되어 있는 패키지로서 머신러닝, 데이터사이언스, 공학 및 그래픽 관련 패키지가 방대하다. 우리는 이러한 Python 패키지를 통계공학을 위한 기본 패키지로 유용하게 사용할 수 있다. 본 논문에서는 통계공학을 위한 Python 패키지 응용을 살펴보고 통계공학 관련 종합 Python projects가 필요함을 제안하였다.

Statistical engineering contains design of experiments, quality control/ management, and reliability engineering. Python is a free software environment for machine learning, data science, and graphics. Python package has many functions and libraries for statistical engineering. We can use Python package as a useful tool for statistical engineering. This paper shows applications of Python package for statistical engineering and suggests a total Python projects for statistical engineering.

키워드

참고문헌

  1. Belorkar A, Cuntuku SC, Hora S, and Kumar A (2020). Interactive Data Visualization with Python, Packt Publishing, Birmingham.
  2. Jang DH (2020). Applications of R package for statistical engineering, The Korean Journal of Applied Statistics, 33, 87-105. https://doi.org/10.5351/KJAS.2020.33.1.087
  3. Lazar D (2020). Pandas Equivalent of 10 Useful SQL Queries, Available from: https://towardsdatascience.com/pandas-equivalent-of-10-useful-sql-queries-f79428e60bd9
  4. Mayo M (2020). Top Python Libraries for Data Science, Data Visualization and Machine Learning, Available from: https://www.kdnuggets.com/2020/11/top-python-libraries-data-science-data- visualization-machine-learninghtml
  5. McKinney W (2018). Python for Data Analysis(2nd ed.), O'Reilly, Boston.
  6. Myers RH, MontGomery DC, and Anderson-Cook CM (2016). Response Surface Methodology(4th ed.), John Wiley & Sons, Inc., Hoboken.
  7. Piepenbreier N (2020). Visualizing COVID-19 Data Beautifully in Python, available from: htps://towardsdatascience.com/ visualizing-covid-19-data-beautifully-in-python-in-5-minutes-or-less-affc361b2c6a
  8. Python Library Altair, Available from: https://altair-viz.github.io
  9. Python Library Bokeh, Available from: https://bokeh.org
  10. Python Library Lifelines, Available from: https://lifelines.readthedocs.io/en/latest/index.html
  11. Python Library Matplotlib, Available from: https://matplotlib.org
  12. Python Library NumPy, Available from: https://numpy.org
  13. Python Library Pandas, Available from: https://pandas.pydata.org
  14. Python Library plotly, Available from: https://plotly.com/python
  15. Python Library pyDOE2, Available from: https://pypi.org/project/pyDOE2/
  16. Python Library pyspc, Available from: https://pypi.org/project/pyspc/
  17. Python Library Reliability, Available from: https://pypi.org/project/reliability/
  18. Python Library scikit-learn, Available from: https://scikit-learn.org/stable
  19. Python Library scipy, Available from: https://www.scipy.org
  20. Python library seaborn, Available from: https://seaborn.pydata.org
  21. Python library statsmodels, Available from: https://www.statsmodels.org/stable
  22. Python package index, Available from: https://pypi.org
  23. Space shuttle challenger disaster, Available from: https://en.wikipedia.org/wiki/Space Shuttle Challenger disaster