Regression Analysis models relationships between response variables and predictors so teams can estimate effects, predict outcomes, and test process drivers.
Definition
Regression Analysis is a statistical method for modeling the relationship between a response variable and one or more predictor variables. It estimates how the response changes as predictors change, while quantifying uncertainty and model fit.
In improvement work, regression helps test suspected drivers, build prediction models, adjust for covariates, and support optimization.
History
Regression methods developed in statistics and became widely used in engineering, economics, quality, healthcare, and operations. Six Sigma practitioners use regression in Analyze and Improve phases, often alongside DOE and hypothesis testing.
When to Use
Use Regression Analysis when the goal is to understand or predict a continuous response, quantify relationships, screen process drivers, model settings, or adjust for multiple inputs. Use logistic or other specialized models when the response is binary, count, or non-normal.
Step-by-Step
- Define the response, predictors, and practical question.
- Collect representative data with reliable measurements.
- Plot relationships and check for outliers or data errors.
- Fit an appropriate regression model.
- Check residuals, model assumptions, multicollinearity, and influential points.
- Interpret coefficients in process terms.
- Validate predictions with holdout data or confirmation runs.
- Use the model to guide action, not replace process knowledge.
Examples
- Process: Temperature and pressure predict adhesive strength.
- Service: Request complexity and staffing predict cycle time.
- Quality: Regression identifies which settings most affect dimension drift.
Common Pitfalls
- Assuming correlation proves causation.
- Ignoring nonlinear relationships or interactions.
- Using unreliable historical data without context.
- No residual or assumption checks.
- Overfitting too many predictors.
- Extrapolating beyond observed conditions.