Hypotheses represent expected or possible answers to key questions within a process. Hypothesis Testing is a rigorous statistical endeavor consisting of determining the existence of sufficient evidence to reject a hypothesis (called the null hypothesis).
The process involves collecting data from one or more populations and performing a statistical test. This method is often mandatory to scientifically demonstrate the potential causes suspected of generating large variations in the output parameters of processes. It is a critical tool in the “Analyze” phase of Six Sigma projects, transforming assumptions into mathematical certainties.
Essential Information:
- Duration: 3 days
- Recommended participants: 5-10
- Level: Medium – Advanced / Statistical Analysis
Target Audience:
- Technical staff from production, quality, engineering, maintenance, logistics, or management departments.
- Process engineers and Lean and Six Sigma specialists.
- Personnel directly involved in identifying root causes for problematic processes.
- Professionals from fields such as production, IT, healthcare, or financial services.
- Entrepreneurs and consultants who wish to base decisions on data.
Learning Objectives
At the end of the training, participants will be able to:
- Understand the role of inferential statistics in process control and improvement.
- Use basic statistical elements (Mean, Variance, P-value, Alpha, Confidence Interval, etc.).
- Identify and use the most appropriate statistical tests to compare processes.
- Determine if differences observed in processes are statistically significant or just accidental.
- Use Minitab software for statistical analysis and result interpretation.
Course Structure
- Introduction to Inferential Statistics: Basic concepts and the role of hypothesis testing.
- The Hypothesis Testing Process: Null Hypothesis (H0) vs. Alternative Hypothesis (Ha).
- Errors in Testing: Alpha and Beta risks, P-value, and Significance Level.
- Tests for Continuous Data:
- 1-Sample and 2-Sample T-Test.
- Paired T-Test.
- One-Way ANOVA.
- Test for Equal Variances.
- Tests for Attribute Data: Chi-Square Test.
- Relationship Analysis: Scatter Plot, Correlation, Simple Regression, and Multiple Regression.
- Practical Applications: Interpretation of case studies and simulations in MINITAB.
- Summary and final discussions.
Organizational Benefits
- Eliminating Variation: Identifying and eliminating unwanted variabilities in product quality.
- Efficiency in Analysis: Increasing the speed of identifying real problems through systematic methods.
- Fact-Based Decisions: Making informed decisions, essential in Six Sigma or process re-engineering projects.
- Scrap Reduction: Identifying factors that contribute to defects and implementing correct preventive measures.
- Team Development: Improving employees’ analytical skills, transforming them into problem-solving experts.
- Competitive Advantage: Increasing customer satisfaction through stable, predictable, and optimized processes.
We recommend participants have access to a laptop with MINITAB software installed (trial version available). It can be downloaded from the MINITAB website: https://www.minitab.com/en-us/products/minitab/free-trial/
The course will conclude with a multiple-choice examination, and participation statements or graduation certificates will be issued, as applicable.
Please contact us for further information or personalized offers.
- 📧 Email: george@sanconsulting.ro
- 📞 Phone: +40 723093515
- 🌐 Web: sanconsulting.ro/contact