Purpose and applicability
Which business characteristics predict customer churn?
No completed-result prerequisite is required for the first run.
Do not use it merely because it is available
Use Binary logistic regression only when its estimand, data roles and assumptions answer the stated research question. Choose a related estimator or diagnostic when the intended outcome is not among the documented outputs below.
Study preparation
- Teaching data
- business-outcomes-observed.csv
- Observations
- 500
- Study type
- Deterministic synthetic teaching study
- Calculate route
- Calculate → Regression → Binary logistic
Open the data dictionary and variable-role reference
Variable roles
Outcome: customer_churn. Predictors: business_performance, market_turbulence, analytics_capability, and organizational_agility. Controls: firm_age_years and employee_count_log
Exact workflow
Download business-outcomes-observed.csv. Keep both files in a writable study folder.
Start QuickPLS, choose New Project, open Data, choose Import Data, select the CSV, review the preview, and choose Import.
Open Data and confirm that the study contains 500 observations. Return to the model or calculation workspace.
Assign the method roles exactly as follows: Outcome: customer_churn. Predictors: business_performance, market_turbulence, analytics_capability, and organizational_agility. Controls: firm_age_years and employee_count_log.
Confirm the selected variables exclude case identifiers and include every role required by the method.
Choose Calculate, then use Calculate → Regression → Binary logistic.
Review the essential settings listed below. Open Advanced settings only when the design requires a non-default option.
Resolve actionable blockers, review applicability warnings, and leave Open Results when finished selected.
Choose Start calculation. Wait for Completed, then confirm that Results opens for this method.
Inspect the named outputs below in order. Use Save Report, table Copy, or Export as required, then save and reopen the project to confirm the result remains available.
Essential and advanced settings
- Confirm customer_churn is coded strictly 0/1
- Keep the default classification cutoff for the first run
Keep other advanced controls at their documented defaults unless the study design requires a justified change. Record every non-default value in the research log.
Results to inspect
- Coefficients and odds ratios
- Model fit
- Classification/confusion table
- ROC/AUC and cutoff chart
Interpretation guidance
For Binary logistic regression, interpret the listed outputs together with the prerequisite result, the selected settings, model assumptions and data quality. The supplied values are instructional; they are not validation against an external paper or another software package.
Reporting guidance
Report the QuickPLS version, Binary logistic regression route, sample size, model or variable roles, preprocessing, essential settings, non-default advanced settings, and the named primary outputs. Retain the project, data checksum and exported table used in the manuscript.
Common mistakes and recovery
- Running the method before completing its prerequisite calculation.
- Changing the data, model or variable roles after the prerequisite result was saved.
- Treating an unavailable or not-applicable value as zero.
- Reporting an estimate without its method-appropriate uncertainty or diagnostic context.
Screen-by-screen evidence
Images shown here are mapped to this tutorial’s required installed-application evidence. Missing captures are labelled explicitly and are not replaced with generic screenshots.
Required screen 1: Imported teaching dataset is open
Required screen 2: Method-specific variable roles are selected
Required screen 3: Correct method and required settings are visible
Required screen 4: Primary Binary logistic regression result is visible
Required screen 5: Second important Binary logistic regression result is visible