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A Study on Prediction of Linear Relations Between Variables According to Working Characteristics Using Correlation Analysis

  • Received : 2022.10.25
  • Accepted : 2022.11.05
  • Published : 2022.11.30

Abstract

Many countries around the world using ICT technologies have various technologies to keep pace with the 4th industrial revolution, and various algorithms and systems have been developed accordingly. Among them, many industries and researchers are investing in unmanned automation systems based on AI. At the time when new technology development and algorithms are developed, decision-making by big data analysis applied to AI systems must be equipped with more sophistication. We apply, Pearson's correlation analysis is applied to six independent variables to find out the job satisfaction that office workers feel according to their job characteristics. First, a correlation coefficient is obtained to find out the degree of correlation for each variable. Second, the presence or absence of correlation for each data is verified through hypothesis testing. Third, after visualization processing using the size of the correlation coefficient, the degree of correlation between data is investigated. Fourth, the degree of correlation between variables will be verified based on the correlation coefficient obtained through the experiment and the results of the hypothesis test

Keywords

References

  1. S. Lee and M. Ko, "Exploring the Key Technologies on Next Production Innovation," Journal of the Korea Convergence Society, vol. 9, no. 9, pp. 199-207, Sep 2018. DOI: https://doi.org/10.15207/JKCS.2018.9.9.199
  2. Y. Jeong, E. Lee, and J. Do, "Development and evaluation of AI-based algorithm models for analysis of learning trends in adult learners," Journal of The Korean Association of Information Education, vol. 25, no. 5. Korean Association of Information Education, pp. 813-824, Oct 2021. DOI: https://doi.org/10.14352/jkaie.2021.25.5.813
  3. D. H. Lee, S. Kim, K. I. Jang, T. Sa, and D. Yoo, "Study on Agricultural Science Convergence R&D Agenda under the Fourth Industry Revolution," The Journal of the Korea Contents Association, vol. 19, no. 7, pp. 323-334, 2019(Jul). DOI: https://doi.org/10.5392/JKCA.2019.19.07.323
  4. S. Yang and Y. S. Lee, "Study on AI-based content reproduction system using movie contents," Journal of Korea Multimedia Society, vol. 24, no. 2, pp. 336-343, Feb 2021. DOI: https://doi.org/10.9717/kmms.2020.24.2.336
  5. S. H. Kim, Y. H. Kang, and D. H. Yoon, "Implementation of Monitoring System of the Living Waste based on Artificial Intelligence and IoT," Journal of IKEEE, vol. 24, no. 1, pp. 302-310, Mar 2020 . DOI: https://doi.org/10.7471/ikeee.2020.24.1.302
  6. S. H. An and O. R. Jeong, "A Study on the Psychological Counseling AI Chatbot System based on Sentiment Analysis," Journal of Information Technology Services, vol. 20, no. 3, pp. 75-86, Jun 2021. DOI: https://doi.org/ 10.9716/KITS.2021.20.3.075
  7. S. J. Lee, "Big Data Analysis Using Principal Component Analysis," Journal of Korean Institute of Intelligent Systems, vol. 25, no. 6. Korean Institute of Intelligent Systems, pp. 592-599, Dec 2015. DOI: https://doi.org/10.5391/JKIIS.2015.25.6.592
  8. C. N. Jun and I. W. Seo, "Analyzing the Bigdata for Practical Using into Technology Marketing : Focusing on the Potential Buyer Extraction," Korean Strategic Marketing Association, Vol. 21, No. 2(58), pp. 181-203. Jun 2013. UCI : G704-001657.2013.21.2.008
  9. M. S. Suh and D. H. Kim, "A Study on the Changing Direction of FinTech Service Model based on Big Data," Global e-Business Association, Vol. 20, No. 2, pp. 195-213, Apr 2019. DOI: https://doi.org/10.20462/TeBS.2019.4.20.2.195
  10. C. Choi and D. Park, "The Analysis of the APT Prelude by Big Data Analytics," Journal of the Korea Institute of Information and Communication Engineering, vol. 20, no. 6, pp. 1129-1135, Jun 2016. DOI: httpss://doi.org/10.6109/jkiice.2016.20.6.1129
  11. H. S. Lee, E. A. Kwak and D. S. Han, "A Study on Factors Affecting Avoidance of Based on Big Data AI Retargeting Advertising", Korean Association for Advertising and Public Relations, Advertising Research (120), pp. 80-111, Mar 2019. DOI: http://dx.doi.org/10.16914/ar.2019.120.80
  12. In-Seon Kim, Chi-Seo Jeong, Tea-Won Jung, Jin-Kyu Kang and Kye-Dong Jung, "AR Tourism Recommendation System Based on Character-Based Tourism Preference Using Big Data", international Journal of Internet, Broadcasting and Communication. Vol. 13. NO. 1. pp. 61-68. 2021. DOI: https://dx.doi.org/10.7236/IJIBC.2021.13.1.61
  13. T. M. Mitchell, "The discipline of machine learning," Carnegie Mellon University, School of Computer Science, Machine Learning Department, 2006. http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf
  14. Albertsson, Kim and et al, "Machine learning in high energy physics community white paper." Journal of Physics: Conference Series. Vol. 1085. No. 2. IOP Publishing, 2018. DOI: https://doi.org/10.1088/1742-6596/1085/2/022008
  15. Mahesh and Batta, "Machine learning algorithms-a review," International Journal of Science and Research (IJSR), Vol. 9, Issus. 1. pp. 381-386. Jan 2020. DOI: https://doi.org/10.21275/ART20203995
  16. Y. H. Oh, H. Kim, J. S. Yun and J. S. Lee, "Using Data Mining Techniques to Predict Win-Loss in Korean Professional Baseball Games," Journal of the Korean Institute of Industrial Engineers(KIIE), Vol. 40, No. 1, pp. 8- 17, Feb 2014. DOI: http://dx.doi.org/10.7232/JKIIE.2014.40.1.008
  17. Zhang, Shichao, Chengqi Zhang, and Qiang Yang, "Data preparation for data mining," Applied artificial intelligence 17. 5-6. pp. 375-381. 2003. DOI: https://doi.org/10.1080/713827180
  18. J. W. Hwa and C. Y. Park, "Variable Selection in Linear Discriminant Analysis", Journal of The Korean Data Analysis Society(JKDAS), Vol.11, No.1 (B), pp. 381-389, Feb 2009. UCI : G704-000930.2009.11.1.020 G704-000930.2009.11.1.020
  19. J. H. Kwon and E. H. Lee, "Predicting Game Addiction in Adolescents: An Application of Discriminant Function Analysis", THE KOREAN JOURNAL OF HEALTH PSYCHOLOGY, The Korean Psychological Association, Vol. 10, NO. 1, pp. 95-112, Mar 2005. https://kiss.kstudy.com/thesis/thesis-view.asp?key=2434958
  20. Y. J. Kim, J. W. Ryu, W. M. Song and M. W. Kim, "Fire Probability Prediction Based on Weather Information Using Decision Tree", Journal of KIISE, JOK: software and application", Vol. 40, No. 11, Nov 2013. UCI: G704-E00398.2013.40.11.003
  21. Choi and Jeong-Il, "Synchronization Phenomenon and Correlation Analysis of Global Stock Market," The Journal of the Korea Contents Association, vol. 16, no. 1, pp. 699-707, Jan 2016. DOI: https://doi.org/10.5392/JKCA.2016.16.01.699
  22. Weenink, David, "Canonical correlation analysis," Proceedings of the Institute of Phonetic Sciences of the University of Amsterdam. Vol. 25. Amsterdam: University of Amsterdam, 2003. http://graphics.stanford.edu/courses/cs233-18-spring/ReferencedPapers/CCA_Weenik.pdf
  23. Rasiwasia, Nikhil and et al, "Cluster canonical correlation analysis," Artificial intelligence and statistics. PMLR, 2014. DOI: https://doi.org/10.7232/iems.2012.11.2.134