The present paper delves into the critical aspects of experimental design and analysis in industrial applications, exploring the strategies employed to optimize manufacturing processes and enhance product quality. It begins by outlining the fundamental principles of experimental design, emphasizing the importance of identifying appropriate variables, determining the optimal experimental conditions, and ensuring the reproducibility of the results. The paper then proceeds to discuss various statistical methods used for analyzing experimental data, including regression analysis, ANOVA (Analysis of Variance), and factor analysis. These techniques are applied to real-world industrial scenarios to illustrate their practical relevance and effectiveness. The paper further examines the challenges faced in implementing experimental design in industrial settings, such as the complexity of the systems, the presence of noise and outliers, and the need for cost-effectiveness. The study concludes with a discussion on the future trends and opportunities in experimental design and analysis, highlighting the potential for advancements in artificial intelligence and machine learning to revolutionize the field.
White, S. (2020). Experimental Design and Analysis in Industrial Applications. Operations Research and Statistics, 2(1), 10. doi:10.69610/j.ors.20200623
ACS Style
White, S. Experimental Design and Analysis in Industrial Applications. Operations Research and Statistics, 2020, 2, 10. doi:10.69610/j.ors.20200623
AMA Style
White S. Experimental Design and Analysis in Industrial Applications. Operations Research and Statistics; 2020, 2(1):10. doi:10.69610/j.ors.20200623
Chicago/Turabian Style
White, Sarah 2020. "Experimental Design and Analysis in Industrial Applications" Operations Research and Statistics 2, no.1:10. doi:10.69610/j.ors.20200623
Share and Cite
ACS Style
White, S. Experimental Design and Analysis in Industrial Applications. Operations Research and Statistics, 2020, 2, 10. doi:10.69610/j.ors.20200623
AMA Style
White S. Experimental Design and Analysis in Industrial Applications. Operations Research and Statistics; 2020, 2(1):10. doi:10.69610/j.ors.20200623
Chicago/Turabian Style
White, Sarah 2020. "Experimental Design and Analysis in Industrial Applications" Operations Research and Statistics 2, no.1:10. doi:10.69610/j.ors.20200623
APA style
White, S. (2020). Experimental Design and Analysis in Industrial Applications. Operations Research and Statistics, 2(1), 10. doi:10.69610/j.ors.20200623
Article Metrics
Article Access Statistics
References
Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
Cook, R. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis for field settings. Houghton Mifflin Company.
Box, G. E. P., Draper, N. R., Hunter, W. G., & Hunter, J. S. (1978). Statistics for experimenters: Design, innovation, and discovery. John Wiley & Sons.
Joreskog, K. G., & Sorbom, D. (1996). LISREL 8: User's guide. Scientific Software International.
Kline, S., & Weng, J. (1995). Experimental design in manufacturing: A review. Journal of Quality Technology, 27(3), 191-213.
Muller, P. E., & Draper, N. R. (1983). Response surface methodology: Process and product optimization using designed experiments. John Wiley & Sons.
Smith, A. F. M., & Deaton, A. (1994). An introduction to design and analysis of experiments. Marcel Dekker.
Box, G. E. P., Hunter, W. G., & Hunter, J. S. (2005). Statistics for experimenters: Design, innovation, and discovery (2nd ed.). Wiley-Interscience.
Montgomery, D. C. (2012). Design and analysis of experiments (7th ed.). John Wiley & Sons.
Snee, R. D. (1989). Response surface methodology: State of the art, state of the science. Technometrics, 31(2), 127-140.
