Regression in SmartPLS 4.0

Paper-Based Learning Track

This learning track uses the shared paper as the main reference case:

Margalina, V.-M., Kreienbaum, C., Hair, J. F., Becker, J.-M., & Ringle, C. M. (2026). Multiple linear and logistic regression analysis: A SmartPLS 4 software tutorial. Journal of Marketing Analytics. https://doi.org/10.1057/s41270-026-00466-2

Why This Paper Is Useful

The paper is directly aligned with this course because it explains how to run and assess multiple linear regression and logistic regression in SmartPLS 4. It is not only theoretical; it provides a software tutorial workflow, explains model design, discusses assumptions, and shows how SmartPLS outputs support result interpretation.

What This Track Adds

Use this folder when you want a paper-guided course:

  1. Paper Explainer
  2. Six-Stage Regression Workflow
  3. HBAT Case Study Variable Map
  4. SmartPLS Click Path Guide
  5. Results Assessment Workbook
  6. Logistic Regression Reporting Template
  7. Optional Module 13: Logistic Regression in SmartPLS 4
  1. Read the paper explainer.
  2. Study the six-stage workflow.
  3. Import the HBAT-style synthetic dataset.
  4. Run multiple linear regression.
  5. Assess model fit, coefficients, collinearity, and residual assumptions.
  6. Run logistic regression.
  7. Assess fit indices, pseudo R-square, confusion matrix, Wald tests, and odds ratios.
  8. Write a complete results section.

Note on Data

The original paper uses the HBAT case from Hair et al. The dataset in this repository is a synthetic HBAT-style teaching dataset with similar variable structure. It is provided only for practice and should not be treated as the original HBAT dataset.