10. Logistic Regression Predicting Quality in Manufacturing.
Title: Logistic Regression for Predicting Quality in Manufacturing — Enhancing Precision through Data Science
📌 Video Description:
This video has been carefully produced with full respect for copyright and intellectual property laws. All elements — including narration, statistical models, and visualizations — are original creations or sourced from open-access and public-domain resources. We do not use any copyrighted material to ensure compliance with legal and ethical standards in academic and professional communication.
⚙️ About this Video:
Discover how logistic regression is a vital tool for predicting product quality within manufacturing environments. By applying this statistical method, organizations can classify product outcomes, predict defects, and optimize quality control processes — ensuring operational excellence and minimizing waste.
📊 Key Concepts Covered:
• Introduction to logistic regression and its role in classification problems.
• Applications in defect prediction and quality assurance.
• Integrating AI and machine learning for automated quality control.
• Case studies in manufacturing efficiency and predictive analytics.
🏭 In today’s data-driven manufacturing sector, predictive models like logistic regression empower businesses to maintain high product standards and achieve continuous improvement.
🎓 Reference Bibliography:
1. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
2. Montgomery, D. C. (2013). Statistical Quality Control: A Modern Introduction (7th ed.). Wiley.
3. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (1st ed.). Springer.
4. Juran, J. M., & Godfrey, A. B. (1999). Juran’s Quality Handbook (5th ed.). McGraw-Hill.
5. Zhang, H., & Jiang, Y. (2017). Machine Learning in Quality Control (1st ed.). Springer.
🔒 For educational purposes only. All rights are respected.
#LogisticRegression #ManufacturingQuality #DataScience #PredictiveAnalytics #AI #OpenAI #EducationalContent
Title: Logistic Regression for Predicting Quality in Manufacturing — Enhancing Precision through Data Science
📌 Video Description:
This video has been carefully produced with full respect for copyright and intellectual property laws. All elements — including narration, statistical models, and visualizations — are original creations or sourced from open-access and public-domain resources. We do not use any copyrighted material to ensure compliance with legal and ethical standards in academic and professional communication.
⚙️ About this Video:
Discover how logistic regression is a vital tool for predicting product quality within manufacturing environments. By applying this statistical method, organizations can classify product outcomes, predict defects, and optimize quality control processes — ensuring operational excellence and minimizing waste.
📊 Key Concepts Covered:
• Introduction to logistic regression and its role in classification problems.
• Applications in defect prediction and quality assurance.
• Integrating AI and machine learning for automated quality control.
• Case studies in manufacturing efficiency and predictive analytics.
🏭 In today’s data-driven manufacturing sector, predictive models like logistic regression empower businesses to maintain high product standards and achieve continuous improvement.
🎓 Reference Bibliography:
1. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). Wiley.
2. Montgomery, D. C. (2013). Statistical Quality Control: A Modern Introduction (7th ed.). Wiley.
3. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning (1st ed.). Springer.
4. Juran, J. M., & Godfrey, A. B. (1999). Juran’s Quality Handbook (5th ed.). McGraw-Hill.
5. Zhang, H., & Jiang, Y. (2017). Machine Learning in Quality Control (1st ed.). Springer.
🔒 For educational purposes only. All rights are respected.
#LogisticRegression #ManufacturingQuality #DataScience #PredictiveAnalytics #AI #OpenAI #EducationalContent
