This course builds upon the foundational skills from Data Driven Insights and Decisions, focusing on advanced statistical techniques essential for delegates, particularly those from Manufacturing and Technical environments. Topics include Gauge R&R Studies (continuous data), Multiple Regression, Process Capability with transformed data, and an introduction to Design of Experiments.

Target Audience:

• Individuals progressing from Data Driven Insights and Decisions or similar foundational training.
• Participants in the Lean Six Sigma Black Belt Programme seeking to deepen their statistical toolkit.

Delivery Methods:

• Virtual or Face-to-Face Classroom Training: Includes 24-month access to self-study video materials.
• Self-Study E-Learning Modules: Equivalent to 2 days of classroom training, with interactive video modules and 1-1 support from a Catalyst tutor.

Software Requirements:

• Minitab Software: Necessary for hands-on exercises. Obtain a copy directly from Minitab or through a free 30-day trial. Alternatively, a 6-month license is available through Catalyst.

Key Learning Outcomes:

• Gauge R&R Studies: Proficiency in planning, executing, and interpreting Gauge R&R and Gauge Linearity Studies.
• Multiple Regression: Extending regression techniques to multiple variables, assessing model quality, handling multicollinearity, and making predictions.
• Process Capability: Understanding Cp/CpK indices with non-normal data and the Six Sigma Shift concept.
• Introduction to Design of Experiments: Basic principles and applications.

Course Contents:

Measurement Systems Analysis

• Gauge R&R Study: Planning, execution, and analysis.
• Gauge Linearity Study: Ensuring linear measurement response.
• Gauge Stability: Assessing consistency over time.

Advanced Statistical Techniques

• Central Limit Theorem: Understanding sampling distributions.
• Combining Distributions: Techniques for data aggregation.
• Box-Cox Transformation: Data normalization techniques.

Process Capability

• Cp/CpK Indices: Assessing process capability with non-normal data.
• Cpm: Evaluating long-term process performance.
• Six Sigma Shift: Adjusting for process drift over time.

Regression Analysis

• Multiple Regression: Model building with multiple predictors.
• Multicollinearity and VIFs: Managing correlated predictors.
• Rsq and Residuals Analysis: Evaluating model fit and residuals.
• Factorial Plots: Visualizing interactions between variables.
• Prediction and Curvilinear Regression: Forecasting based on regression models.

Introduction to Design of Experiments

• Basic principles and objectives.

Follow-on Options:

• Advanced Black Belt Tools: Expanding the statistical toolkit further.
• Lean Six Sigma for Innovation and Design (Design for Lean Six Sigma): Applying Lean Six Sigma principles to innovation and design processes.