Article 257: Logistic Regression in R for Engineering: End-to-End Case Studies and Applications

This article explores how logistic regression can be applied in engineering to predict failures, defects, and risks, demonstrating end-to-end workflows in R with practical diagnostics and interpretability to support reliable decision-making.

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Article outline

1. Introduction to Logistic Regression in Engineering

• Brief overview of logistic regression and its importance in engineering classification problems.

• Why logistic regression is useful: interpretability, probability outputs, odds ratios.

• Contextual examples of engineering scenarios where logistic regression applies.

2. Applications of Logistic Regression in Engineering

• Structural Engineering: Predicting the likelihood of component failure under load, temperature, or material conditions.

• Manufacturing Engineering: Identifying whether a manufactured part is defective using operational and process variables.

• Energy Systems Engineering: Classifying operating conditions as safe or risky based on environmental and system load data.

3. Mathematical Foundation of Logistic Regression

• Logistic (sigmoid) function and mapping linear predictors into probabilities.

• Coefficients as log-odds and interpretation via odds ratios.

• Brief mention of estimation via Maximum Likelihood Estimation (MLE) and possible regularization.

4. Setting Up the R Environment

• Installing and loading necessary R packages (stats, caret, pROC, ggplot2, ResourceSelection, etc.).

• Preparing the environment for data simulation, preprocessing, modeling, and visualization.

5. Creating Engineering Datasets in R

• Simulating datasets for three case studies:

  • Structural failure prediction (load, temperature, material quality).

  • Manufacturing defect detection (vibration, pressure, operator skill).

  • Energy system risk classification (humidity, load, temperature).

6. Preprocessing Engineering Data

• Splitting data into training and test sets.

• Scaling predictors where needed.

• Ensuring proper factor encoding for categorical outcomes.

7. Building Logistic Regression Models in R

• Using glm() with family = binomial for logistic regression.

• Training models on engineering datasets.

• Extracting coefficients and interpreting odds ratios.

8. Model Evaluation and Diagnostics

• Confusion matrices and classification reports (caret::confusionMatrix).

• ROC and AUC using pROC.

• Precision–Recall curve.

• Calibration plots and Hosmer–Lemeshow test (ResourceSelection).

• Visualization of odds ratios and feature importance.

9. Case Study 1: Structural Failure Prediction

• Full workflow: preprocessing, training logistic regression, evaluating, and interpreting coefficients.

10. Case Study 2: Manufacturing Defect Classification

• Full workflow applied to manufacturing data with focus on process improvement insights.

11. Case Study 3: Energy Risk Classification

• Full workflow for energy systems with emphasis on risk management and operational safety.

12. Comparison with Alternative Models in R

• Benchmarking logistic regression against Decision Trees (rpart) and Support Vector Machines (e1071).

• Discussion of accuracy, interpretability, and use cases.

13. Advantages and Limitations in Engineering Applications

• Advantages: interpretability, simplicity, probabilistic outputs.

• Limitations: assumption of linear log-odds, sensitivity to multicollinearity, limitations in non-linear relationships.

14. End-to-End R Script

• A unified script that includes dataset creation, preprocessing, logistic regression modeling, diagnostics, visualization, and model comparison.

15. Conclusion

• Key takeaways about applying logistic regression in engineering contexts.

• Emphasis on both predictive performance and interpretability for informed decision-making.

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