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Journals / Gıda ve Yem Bilimi Teknolojisi Dergisi / 2026 / Sayı: 36

A study on the explanation of the advantages of using extreme value analysis in scientific research: The milk powder example study

Bilimsel araştırmalarda uç değer analizi kullanımının sağlayacağı avantajların izahı üzerine bir araştırma: Süt tozu örneği çalışması

Murat Çimen0000-0003-4290-2718
Pages
83–92
Publication Date
2026-07-02
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results and discussion: According to the results obtained through outlier analysis, the highest milk powder production in Turkey between 2010 and 2024 was achieved in 2014, with a value of 5,658 tons in January.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: The data used in this study, which relate to milk powder production in Turkey during January months between 2010 and 2024, were obtained from TURKSTAT (Turkish Statistical Institute).
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Key Findings & Data:Results and discussion: According to the results obtained through outlier analysis, the highest milk powder production in Turkey between 2010 and 2024 was achieved in 2014, with a value of 5,658 tons in January.
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Conclusion & Impact:The current study is the first to emphasize how outlier analysis can be used to identify statistical differences between data sets obtained from agricultural fields.
🔬Design:Modeling & Simulation
📍Scope:2010 Verileri
⚙️Variables:Bilimsel araştırma · outlier analizi · süt tozu

Abstract

Objective: The data used in this study, which relate to milk powder production in Turkey during January months between 2010 and 2024, were obtained from TURKSTAT (Turkish Statistical Institute). The dataset's maximum and minimum extreme values were statistically identified, and the advantages of interpreting these data were highlighted. Materials and methods: The study clearly explained the box plot, normal distribution test, and interpretation of extreme value rank tables used in outlier analysis to obtain extreme data. SPSS was used in these analyses. Results and discussion: According to the results obtained through outlier analysis, the highest milk powder production in Turkey between 2010 and 2024 was achieved in 2014, with a value of 5,658 tons in January. It is noteworthy that the lowest milk powder production occurred in 2011, with a production of 1,343 tons, and the lowest production after that year occurred in 2010, with a production amount of 1,779 tons. The current study demonstrates that it is possible to identify differences in a single data set obtained from any type of scientific research by statistically interpreting the outliers resulting from their deviation from the median. Outlier analysis is a method used extensively in data extraction, determination of standard reference values, statistical control studies, and modeling studies, a popular topic in recent years. The current study is the first to emphasize how outlier analysis can be used to identify statistical differences between data sets obtained from agricultural fields.

Özet

Amaç: Araştırmada kullanılan 2010-2024 yılları arasında Ocak aylarında ülkemizde üretilen süt tozu üretim miktarları ile ilgili değerler TUİK bünyesinden elde edilmiştir. Veri setinde maksimum ve minimum noktalarda oluşan uç verilerin istatistikî olarak belirlenerek bu veriler üzerinden yapılacak yorumların sağlayacağı avantajlar vurgulanmaya çalışılmıştır. Materyal ve Yöntem: Çalışmada uç verilerin elde edilmesinde yararlanılan uç değer (outlier) analizinde kullanılan kutu grafiği, normal dağılım testi, ekstrem değer sıra çizelgelerinin yorumlanması gibi hususların açık şekilde izahına çalışılmıştır. Adı geçen analizlerde SPSS programı kullanılmıştır. Tartışma ve sonuç: Uç değer analizi ile elde edilen sonuçlara göre 2010-2024 yılları arasında Ocak aylarında ülkemizde en yüksek süt tozu üretiminin 5658 tonluk bir değerle 2014 yılında elde edildiği görülmektedir. En düşük süt tozu üretiminin ise 1343 tonluk üretimle 2011 yılında gerçekleştiği, bu yıldan sonraki en düşük üretimin ise 1779 tonluk bir üretim miktarı ile 2010 yılında gerçekleştiği dikkati çekmektedir. Mevcut çalışma, her türlü bilimsel araştırmadan elde edilen tek bir veri setinde, medyana göre gösterdikleri sapma sonucu açığa çıkacak uç değerlerin istatistikî olarak anlamlandırılması ile oluşacak farklılıkların belirlenmesinin mümkün olabileceğini ispatlamaktadır. Outlier analizi veri ayıklama, standart referans değerlerin belirlenmesi, istatistikî kontrol çalışmaları ve son yılların popüler konusu olan modelleme çalışmalarında ağırlıklı olarak kullanılan bir yöntemdir. Mevcut araştırma tarımsal alandan elde edilen veri setlerinde verilerin kendi aralarında istatistikî farklılıklarının tespitinde outlier analizinden nasıl yararlanılabileceğini vurgulayan ilk çalışmadır.

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Cite This Article

Çimen, M. (2026). A study on the explanation of the advantages of using extreme value analysis in scientific research: The milk powder example study. Gıda ve Yem Bilimi Teknolojisi Dergisi, Sayı: 36, 83–92. https://doi.org/10.56833/gidaveyem.1751937

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References (38)

  1. Aggarwal, C.C. (2017). Outlier analysis. Second edition. Pp.466. Springer international publishing. ISBN: 978-3-319-47577-6. DOI: 10.1007/978-3-319-47578-3
  2. Angiulli, F. and Fassetti, F. (2010). Distance-based outlier queries in data streams: the novel task and algorithms. Data Mining and Knowledge Discovery, 20 (2), 290-324. DOI: 10.1007/s10618-009-0159-9
  3. Aydın, M.K. and Keskin, M. (2018). Muğla ilinde küçükbaş hayvan yetiştiriciliğinin yapısal özellikleri. Mediterrenean Agricultural Sciences, 31(3), 317-323. https://doi.org/10.29136/mediterranean. 428577
  4. Babura, B. I. et al (2018). Analysis and assessment of boxplot characters for extreme data. Journal of Physics Conference Series, 1132, 012078, DOI: 10.1088/1742-6596/1132/1/012078
  5. Barnett, V. and Lewis, T. (1994). Outliers in statistical data, 3rd Edition. Pp. 608. Wiley Publication Series. ISBN: 978-0-471-93094-5
  6. Chauhan, P. and Shukla, M. (2015). A review on outlier detection techniques on data stream by using different approaches of K-Means algorithm. International Conference on Advancesn Computer Engineering and Applications, India, 2015, pp. 580-585, DOI: 10.1109/ICACEA.2015.7164758.
  7. Çimen, M. (2015). Fen ve sağlık bilimleri alanlarında spss uygulamalı veri analizi (1.Baskı), Palme Yayıncılık, Yayın No: 905, ISBN: 978-605-355-366-3.Sıhhıye, Ankara.314 s.
  8. Çimen, M. (2016). Mühendislik verilerinde tek örnek için parametrik ve parametrik olmayan testler. İstanbul Aydın Üniversitesi Dergisi, 29, 67-77.
  9. Çimen, M. (2024). A research on the importance of testing the normality assumption in microbiological data. International Advanced Researches and Engineering Journal, 8 (3), 161-166. https://doi.org/10.35860/iarej.1532064
  10. Çimen, M. (2025). Tarımsal üretim verilerinde uç değer analizi yaklaşımı: Manda ve sığır et üretimi örneği. Anadolu Journal of Aegean Agricultural Research Institute, 35(2), 267-279. https://doi.org/10.18615/anadolu.1741396
  11. Çimen, M. and Güven, A. (2025). Examination of Numerical Changes in Goat and Buffalo Population in TRB1 Region with Cluster Analysis. Journal of the Institute of Science and Technology, 15(1), 364-371. https://doi.org/10.21597/jist.1525746
  12. Das, C., Dubey, A. and Rasool, A. (2022). Outlier detection techniques: A comparative study. In: Patgiri, R., Bandyopadhyay, S., Borah, M. D., Emilia Balas, V. (eds) Edge Analytics. Lecture Notes in Electrical Engineering, 869, 551-566. https://doi.org/10. 1007/978-981-19-0019-8_42
  13. Dawson, R. (2023). Fundamentals of data analytics: Learn essential skills, embrace the future, and catapult your career in the data-driven world-a comprehensive guide to for beginners (fundamentals series). Kindle Ed. Pp. 168. ASIN:B0CP3198QX.
  14. Dongre, P. K., Patel, V., Bhoi, U. and Maltare, N. (2025). An outlier detection framework for Air Quality Index prediction using linear and ensemble models. Decision Analytics Journal, 14 (10), 1-13. https://doi.org/10.1016/j.dajour.2025.100546.
  15. Flora, D. B. (2018). Statistical methods for the social and behavioral sciences: a model based approach, Pp. 472. Sage publications. ISBN-10: 1446269833.
  16. King, A. P. and Eckersley, R. (2019). Statistics for biomedical engineers and scientists: How to visualize and analyze data. First Edition. Kindle Ed. Academic Pres. Pp.250. ISBN-13: 978-0081029404.
  17. Krishnamoorthy, K. (2016). Handbook of statistical distributions with applications, 2nd Edition. Pp 383. CRC Press. Taylor and Franchis Group. ISBN-13: 978-1-4987-4150-7.
  18. Kontaki, M., Gounaris, A., Papadopoulos, A. N., Tsichlas, K. and Manolopoulos, Y. (2011). Continuous monitoring of distance-based outliers over data streams. In: The 27th International Conference on Data Engineering (ICDE), pp. 135–146
  19. Lipi, A., Kumar, K. and Chakraborty, S. (2020). Outliers: Detection and analysis. Pp.146. Nova Science Pub Inc. ISBN-10:1685075541
  20. Monjas, A. M., Ruiz, D. R., Rey, D. P. and Palchuk, M. (2023). Automatic outlier detection in laboratory result distributions within a real world data network. Studies in Health Technology and Informatics, 18 (302), 88-92. doi: 10.3233/SHTI230070.
  21. Patel, J.K. and Cambpell, B.R. (1996). Handbook of the normal distribution, (Statistics: A series of textbooks and monographs) 2nd Ed. Pp 456. CRC Press. ISBN-13:978-0824793425
  22. Paulson, D. S. (2003). Applied statistical designs for the researcher (Chapman and Hall/CRC biostatistics series book 12) First Edition. Pp.712. Kindle Ed. CRC Press. ISBN-13: 978-1482276244.
  23. Polat, F. and Kaygısız, M. (2021). Çiğ Süt, Süt Tozu ve Peyniraltı Suyu Tozlarında Gerçek Protein Değeri Tayini ve NPN Tağşişlerinin Önlenmesinde Kullanım Olanaklarının Araştırılması. Gıda Ve Yem Bilimi Teknolojisi Dergisi, 25, 30-38. https://izlik.org/JA82MG92HP
  24. Rousseeuw, P.J. and Leroy, A.M. (2005). Robust regression and outlier detection. Pp. 360. Wiley Publication Series. ISBN: 978-0-471-72537-4
  25. Sadik, S. and Gruenwald, L. (2014). Research issues in outlier detection for data streams. ACM SIGKDD Explorations Newsletter, 15 (1), 33–40.
  26. Sheskin, D. J. (2011). Handbook of parametric and non-parametric statistical procedures, Fifth Edition. Taylor and Francis Group. 6000 Broken South Parkway NW Suite 300 Boca Raton. FI. 33487-2742. ISBN: 978-1-4398-5801-1.
  27. Singh, J. P. (2022). Statistical methods in public health, In Gupta S.D. (eds) health care system managements. Pp. 85-127. Springer. ISBN: 978-981-19-3076-8
  28. Suri, N. N. R., Narasimha, M. M. and Athithan, G. (2019). Outlier detection: Techniques and applications. A data mining perspective. Springer international publishing. 1st Ed. Pp. 214. ISBN: 978-3-030-05127-3.
  29. Tan, S. (2022). Türkiye’de süt tozu üretimi ve rekabet şansı. Tarım ve Köyişleri Bakanlığı Tarımsal Ekonomi Araştırma Enstitüsü Yayın no: 86. https://kutuphane.tarimorman.gov.tr/vufind/Record/13909
  30. Thakkar, P., Vala, J. and Prajapati, V. (2016). Survey on outlier detection in data stream. International Journal of Computer Applications, 136, 13-16.
  31. Thomopoulos, N.T. (2018). Probability distributions: With truncated, log and bivariate extensions 1st Ed. Pp. 163. Springer Cham. ISBN978-3-319-76041-4.
  32. TUIK, (2025). Türkiye İstatistik Kurumu. 2010-2024 yılları arası Türkiye süt tozu üretim verileri. Ankara. https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=1 Date Accessed 20.06. 2025.
  33. Tüney Bebek, D. and Keskin, M. (2021). Some reproductive, fattening and milk yield traits of South Karaman sheep. Mustafa Kemal Üniversitesi Tarım Bilimleri Dergisi, 26(1), 1-7. ISSN:1300-9362. https://doi.org/10.37908/ mkutbd.729230
  34. Vieira, E. T. (2017). Introduction to real world statistics: with step-by-step spss ınstructions. Routledge pub. 1st Edition. Pp. 628. Kindle Ed. ISBN-13: 978-1351869812
  35. Wang Y., Chen, Z., Liu, J. and Wang H. (2025). Outlier identification method based on multi-model weighted consensus in conjunction with Monte Carlo Cross-Validation. Journal of AOAC International, 23, 61-77. doi: 10.1093/jaoacint/qsaf061.
  36. Williamson, D. (2024). Mastering outliers in data analysis: A practical guide using excel and R. Pp. 156. Kindle Ed. First Edition. ASIN:B0DB4N7LV7
  37. Ye, H., Kitagawa, H. and Xia, J. (2015) Continuous angle-based outlier detection on high-dimensional data streams. In: 19th International Database Engineering and Applications Symposium, pp. 162–167.
  38. Dongre, P. K., Patel, V., Bhoi, U. and Maltare, N. (2025). An outlier detection framework for Air Quality Index prediction using linear and ensemble models. Decision Analytics Journal, 14 (10), 1-13. https://doi.org/10.1016/j.dajour.2025.100546.