Course Length: 3 Days

## Course Overview

Statistical thinking is necessary in any effective process improvement endeavor in any organization. All business improvement professionals know how important sound quantitative analysis is, yet statistics can be challenging to learn and use properly.

Throw on top of that a complex statistical analysis software package that you also need to learn and remember, and suddenly you find the whole concept very challenging to use. The ability to make low risk decisions using only samples of data will come to the fore with the growing need to improve faster and compete more effectively.

That's why this Minitab Statistical Analysis course was put together to remove the pain of finding different functions and trying to remember how to use them for the purpose of providing business improvement practitioners and line managers with a simple guide to the use of Minitab Software in the application of the statistical analysis tools of Six Sigma.

**Target Audience**

Lean Six Sigma Professionals

**Course Outline**

**Common Functions**

- Importing Excel Files
- Changing Data Types (Text and Numeric Data)
- Stacking Columns
- Unstacking Columns
- Column Statistics
- Minitab Shortcuts

**P Values – A Summary**

- A Summary of P Values and Hypothesis

**Descriptive Statistics**

- Descriptive Stats Summary with Histogram

**Capability Analysis**

- Terminology
- Undertaking Basic Capability Analysis
- Cp/Cpk – Potential (Short Term) Capability Indices
- Pp/Ppk – Long Term Capability Indices
- Capability Analysis Six Pack

**Normality Testing**

- Subjective Testing with Histograms and Probability Plots
- Statistical Testing with Anderson Darling Normality Test

**Gage Repeatability and Reproducibility**

- Overview of Gage R&R
- Creating the Gage R&R Study Worksheet
- Gage R&R with Minitab (Crossed)
- Interpreting Results

**Frequency Distributions – Categorical Data**

- Pie Chart with Minitab
- Stratified Pie Charts with Minitab
- Pareto Charts with Minitab

**Frequency Distributions – Numerical Data**

- Basic Histogram with Minitab
- Stratified Histograms with Minitab
- Simple Boxplot with Minitab – Singly Y
- Stratified Boxplots with Minitab – Singly Y
- ANOM Chart for Means – Normal Data Only
- ANOM Chart for Binomial and Poisson Data

**MS Excel Pivot Tables / Pivot Chart Reports**

- Pivot Table with MS Excel
- Setting Up the Pivot Chart

**Data Transformation**

- Box Cox Transformation

**Capability Analysis (With Transformation)**

- Capability Analysis (With Transformation)

**Run Charts**

- Run Chart with Minitab
- Using P Values with Run Chart Non-Parametric Tests

**Control Charts**

- Setting Minitab’s Control Chart defaults
- Choosing The Relevant Control Chart
- Variable Data – I-MR Chart
- Variable Data – Xbar-R Chart
- Variable Data – Xbar-S Chart
- Variable Data – Separating Data into Stages on the Same Chart
- Variable Data – Zone Chart
- Variable Data – CUSUM Chart
- Adding Reference Lines to Control Charts
- Attribute Data – NP Chart
- Attribute Data – P Chart
- Attribute Data – U Chart
- Attribute Data – C Chart

**Hypothesizing **

- The Hypothesis Testing Process
- Choosing Hypothesis Tests – Decision Flowchart
- Summary of Hypothesis Tests and Assumptions
- Stating Hypothesis Test Conclusions
- Chi-Square Test for Association
- 1 Proportion Test
- 2 Proportion Test
- Test for Equal Variances
- 1 sample T-Test
- 1 Sample Sign-Test
- 2 Sample T-Test
- Mann Whitney Test
- Paired T-Test
- One Way ANOVA
- Mood’s Median Test

**Two Way ANOVA / Balanced ANOVA**

- Two Way ANOVA (Balanced AOVA)

**Design of Experiments (Factorial)**

- Check Orthogonality of the Design
- Designing the Experiment
- Analyzing the Experiment
- Reducing the Model
- Changing The Way The Design Is Displayed
- Check Validity of the Model Using Residuals
- Factorial Plots for Designed Experiments
- Response Optimization

**Correlational and Regression**

- Correlation Versus Regression
- The Process
- Visual Correlation Analysis
- Compute Correlation (Includes Matrix Plot)
- Developing a Simple Regression Model
- Check Validity of The Regression Model
- Developing a Multiple Linear Regression Model
- Check Validity of The Regression Model
- Summary of Terms – Linear Regression

**Application Examples**

- Hypothesis Testing – General Application
- Hypothesis Testing – Six Sigma Projects