Description
Lean Six Sigma Analyst
The course can be attended following on from the Green Belt Practitioners’ course or as a stand-alone course for those wanting to know more about statistical analysis using Minitab®.
In the course we consider more advanced techniques required to help analyse process data, interpret the results and apply inferential techniques to measure the risks associated with change and improvement decisions.
Upon completion of the LSSA, delegates are fully equipped to tackle more complex business problems and complement qualitative analysis from the Green Belt (process modelling, VOC analysis, value analysis, root cause analysis) with formal statistical methods (estimation, hypothesis testing and correlation analysis).
Theoretical concepts are supported by hands-on exercises using real project data.
Software used is Minitab®
On the last day of the training, delegates sit on the Statistical Analyst certification exam.
Prerequisites
This course can be used as a development of the Green Belt Practitioners’ course or as a stand-alone introduction to Statistical Analysis, using Minitab®.
Delegates will learn how to
At the end of this course you will be able to understand:
- Quantitative methods for process improvement
- Application of statistical tools to support root cause analysis
- Descriptive and Inferential Statistics techniques and Data Analysis (graphical and formal)
- Use of Minitab® to analyse both continuous and discrete data
Outline
Introduction to Minitab®
- Basic functions and data manipulation within Minitab®
Measure
- Types of data – continuous and discrete
- Sample size
- Measurement Systems Analysis – Attribute Agreement Analysis
- Basic statistics – Mean, Median and Mode; Range and Standard Deviation
- The Central Limit Theorem (CLT)
- Basic graphical analysis within Minitab®
Analyse
- Estimation (point estimation and confidence intervals)
- Hypothesis Testing – Introduction
- Hypothesis Testing Examples and Minitab® Exercises
- Statistical Tests on continuous data (Z and T tests, tests for variances, ANOVA – Analysis of Variance
- Non Normal Data
- Non Parametric Tests
- Tests for discrete variables (proportions, Chi-Square)
- Correlation Analysis and Correlation Indexes (Pearson, Spearman)
- Regression Analysis overview and exercises
Control
- Statistical Process Control (SPC)
- SPC applicability and interpretation of Control Charts
- Charts for continuous data and discrete data
Examination
- There is an exam at the end of the course.
