Minitab for Statistical Data Analysis Training Course

Chapter 1: Descriptive Statistics and Visual Data Analysis

Introduction

1. Learning Objectives
2. Data Categories

Core Concepts

1. Data Classifications
2. Assessment: Data Categories

Visualizing Data Through Graphs

1. Foundational Principles
2. Bar Graphs and Pareto Diagrams
3. Pie Charts
4. Histograms
5. Dot Plots
6. Individual Value Plots
7. Box-and-Whisker Plots
8. Time Series Graphs
9. Assessment: Graphical Data Analysis
10. Minitab Feature: Bar Graph
11. Minitab Feature: Pie Chart
12. Minitab Feature: Histogram
13. Minitab Feature: Dot Plot
14. Minitab Feature: Individual Value Plot
15. Minitab Feature: Box-and-Whisker Plot
16. Minitab Feature: Time Series Graph
17. Practical Task: Graphical Analysis

Statistical Data Examination

1. Foundational Principles
2. Average and Median Values
3. Spread, Variability, and Standard Deviation
4. Assessment: Statistical Data Examination
5. Minitab Feature: Displaying Summary Statistics
6. Practical Task: Descriptive Statistics

Wrap-up and Objective Recap

Chapter 2: Inferential Statistics

2.1 Introduction

2.1.1 Learning Objectives
2.2 Essentials of Inferential Statistics
2.2.1 Core Principles
2.2.2 Random Sampling
2.2.3 Assessment: Essentials of Inferential Statistics
2.2.4 Minitab Feature: Random Sampling

2.3 Sampling Distributions

2.3.1 Core Principles
2.3.2 Distribution of the Sample Mean
2.3.3 Assessment: Sampling Distributions

2.4 Gaussian Distribution

2.4.1 Core Principles
2.4.2 Probabilities in a Gaussian Distribution
2.4.3 Probabilities of the Sample Mean
2.4.4 Assessment: Gaussian Distribution
2.4.5 Minitab Feature: Cumulative Probability for Gaussian Distribution
2.4.6 Practical Task: Probabilities and Gaussian Distributions

2.5 Summary

2.5.1 Objective Recap

Chapter 3: Hypothesis Testing and Confidence Bounds

3.1 Introduction

3.1.1 Learning Objectives

3.2 Testing and Confidence Limits

3.2.1 Confidence Intervals
3.2.2 Hypothesis Testing Principles
3.2.3 Applying Hypothesis Testing for Decision Making
3.2.4 Type I and Type II Errors and Statistical Power
3.2.5 Assessment: Testing and Confidence Limits

3.3 One-Sample t-Test

3.3.1 Core Principles
3.3.2 Individual Value Plots
3.3.3 One-Sample t-Test Outcomes
3.3.4 Underlying Assumptions
3.3.5 Assessment: One-Sample t-Test
3.3.6 Minitab Feature: One-Sample t-Test
3.3.7 Practical Task: One-Sample t-Test

3.4 Two-Sample Variance Test

3.4.1 Core Principles
3.4.2 Box-and-Whisker Plots
3.4.3 Two-Sample Variance Test Results 3.4.4 Underlying Assumptions
3.4.5 Assessment: Two-Sample Variance Test
3.4.6 Minitab Feature: Two-Sample Variance Test
3.4.7 Practical Task: Two-Sample Variance Test

3.5 Two-Sample t-Test

3.5.1 Core Principles
3.5.2 Individual Value Plots
3.5.3 Two-Sample t-Test Outcomes
3.5.4 Underlying Assumptions
3.5.5 Assessment: Two-Sample t-Test
3.5.6 Minitab Feature: Two-Sample t-Test
3.5.7 Practical Task: Two-Sample t-Test

3.6 Paired t-Test

3.6.1 Core Principles
3.6.2 Individual Value Plots
3.6.3 Paired t-Test Outcomes
3.6.4 Underlying Assumptions
3.6.5 Assessment: Paired t-Test
3.6.6 Minitab Feature: Paired t-Test
3.6.7 Practical Task: Paired t-Test

3.7 One Proportion Test

3.7.1 Core Principles
3.7.2 One Proportion Test Outcomes
3.7.3 Underlying Assumptions
3.7.4 Assessment: One Proportion Test
3.7.5 Minitab Feature: One Proportion Test
3.7.6 Practical Task: One Proportion Test

3.8 Two Proportions Test

3.8.1 Core Principles
3.8.2 Two Proportions Test Outcomes
3.8.3 Underlying Assumptions
3.8.4 Assessment: Two Proportions Test
3.8.5 Minitab Feature: Two Proportions Test
3.8.6 Practical Task: Two Proportions Test

3.9 Chi-Square Test

3.9.1 Core Principles
3.9.2 Chi-Square Test Outcomes
3.9.3 Underlying Assumptions
3.9.4 Assessment: Chi-Square Test
3.9.5 Minitab Feature: Chi-Square Test
3.9.6 Practical Task: Chi-Square Test

3.10 Summary

3.10.1 Objective Recap

Chapter 4: Statistical Control Charts

4.1 Introduction

4.1.1 Learning Objectives

4.2 Statistical Process Control

4.2.1 Core Principles
4.2.2 Identifying Patterns in Control Charts
4.2.3 Assessment: Statistical Process Control

4.3 Control Charts for Variable Data with Subgroups

4.3.1 Core Principles
4.3.2 Range (R) Charts
4.3.3 Standard Deviation (S) Charts
4.3.4 Mean (Xbar) Charts
4.3.5 Assessment: Control Charts for Variable Data with Subgroups
4.3.6 Minitab Feature: Xbar-R Chart
4.3.7 Practical Task: Xbar-R Chart

4.4 Control Charts for Individual Measurements

4.4.1 Core Principles
4.4.2 Moving Range Charts
4.4.3 Individuals Charts
4.4.4 Assessment: Control Charts for Individual Measurements
4.4.5 Minitab Feature: I-MR Chart
4.4.6 Practical Task: I-MR Chart

4.5 Control Charts for Attribute Data

4.5.1 Core Principles
4.5.2 Defective (P) and Defects (C) Charts
4.5.3 C and U Charts
4.5.4 Assessment: Control Charts for Attribute Data
4.5.5 Minitab Feature: P Chart
4.5.6 Practical Task: P Chart

4.6 Wrap-up and Objective Recap

Chapter 5: Process Performance Evaluation

5.1 Introduction

5.1.1 Learning Objectives

5.2 Process Capability for Normal Distributions

5.2.1 Core Principles
5.2.2 Underlying Assumptions
5.2.3 Normality Verification
5.2.4 Assessment: Process Capability for Normal Distributions
5.2.5 Minitab Feature: Normality Test
5.2.6 Practical Task: Assumptions for Process Capability

5.3 Capability Metrics

5.3.1 Intrinsic Capability: Cp and Cpk
5.3.2 Process Performance: Pp and Ppk
5.3.3 Sigma Level
5.3.4 Assessment: Capability Metrics
5.3.5 Minitab Feature: Cp and Pp
5.3.6 Minitab Feature: Sigma Level
5.3.7 Practical Task: Process Capability for Normal Distributions

5.4 Process Capability for Non-Normal Distributions

5.4.1 Data Transformations and Alternative Distributions
5.4.2 Box-Cox Transformation
5.4.3 Johnson Transformation
5.4.4 Alternative Distributions
5.4.5 Assessment: Process Capability for Non-Normal Distributions
5.4.6 Minitab Feature: Box-Cox Transformation
5.4.7 Minitab Feature: Johnson Transformation
5.4.8 Minitab Feature: Capability Analysis via Johnson Transformation
5.4.9 Minitab Feature: Alternative Distributions
5.4.10 Minitab Feature: Capability Analysis with Alternative Distributions
5.4.11 Practical Task: Process Capability with Data Transformations
5.4.12 Practical Task: Process Capability with Alternative Distributions

5.5 Summary

5.5.1 Objective Recap

Chapter 6: Analysis of Variance (ANOVA)

6.1 Introduction and Learning Objectives

6.2 ANOVA Fundamentals

6.2.1 Core Principles
6.2.2 Visuals and Summary Metrics
6.2.3 Assessment: ANOVA Fundamentals

6.3 Single-Factor ANOVA

6.3.1 Hypothesis Testing
6.3.2 F-Ratios and P-Values
6.3.3 Post-Hoc Comparisons
6.3.4 Assumptions and Residual Diagnostics
6.3.5 Assessment: Single-Factor ANOVA
6.3.6 Minitab Feature: Single-Factor ANOVA
6.3.7 Practical Task: Single-Factor ANOVA

6.4 Two-Factor ANOVA

6.4.1 Core Principles
6.4.2 Visuals
6.4.3 Hypothesis Testing
6.4.4 F-Ratios and P-Values
6.4.5 Assumptions and Residual Diagnostics
6.4.6 Assessment: Two-Factor ANOVA
6.4.7 Minitab Feature: Two-Factor ANOVA
6.4.8 Practical Task: Two-Factor ANOVA

6.5 Summary

Chapter 7: Correlation and Regression Analysis

7.1 Introduction

7.1.1 Learning Objectives

7.2 Associations Between Quantitative Variables

7.2.1 Core Principles
7.2.2 Scatter Plots
7.2.3 Correlation Coefficients
7.2.4 Assessment: Associations Between Quantitative Variables
7.2.5 Minitab Feature: Scatter Plot
7.2.6 Minitab Feature: Correlation
7.2.7 Practical Task: Scatter Plots and Correlation

7.3 Simple Linear Regression

7.3.1 Core Principles
7.3.2 Regression Modeling
7.3.3 Hypothesis Testing and Coefficient of Determination (R-squared)
7.3.4 Assumptions and Residual Diagnostics
7.3.5 Assessment: Simple Linear Regression
7.3.6 Minitab Feature: Simple Regression
7.3.7 Practical Task: Simple Regression

7.4 Wrap-up and Objective Recap

Chapter 8: Measurement System Evaluation

8.1 Introduction

8.1.1 Learning Objectives

8.2 Basics of Measurement System Evaluation

8.2.1 Core Principles
8.2.2 Accuracy
8.2.3 Precision
8.2.4 Distinguishing Accuracy from Precision
8.2.5 Assessment: Basics of Measurement System Evaluation

8.3 Repeatability and Reproducibility

8.3.1 Core Principles
8.3.2 Gauge R&R Studies
8.3.3 Assessment: Repeatability and Reproducibility

8.4 Visual Assessment of Gauge R&R Studies

8.4.1 Core Principles
8.4.2 Sources of Variation
8.4.3 Xbar and R Charts
8.4.4 Interaction Between Operator and Component
8.4.5 Comparative Plots
8.4.6 Gauge Run Charts
8.4.7 Assessment: Visual Assessment of Gauge R&R Studies
8.4.8 Minitab Feature: Crossed Gauge R&R Study
8.4.9 Minitab Feature: Gauge Run Chart
8.4.10 Practical Task: Visual Assessment of Gauge R&R Studies

8.5 Variability Analysis

8.5.1 Standard Deviation and Study Variation
8.5.2 Tolerance Limits
8.5.3 Process Variation
8.5.4 Assessment: Variability Analysis
8.5.5 Practical Task: Numerical Analysis of Gauge R&R Studies

8.6 ANOVA in Gauge R&R Studies

8.6.1 Variance Components
8.6.2 ANOVA Tables
8.6.3 Assessment: ANOVA in Gauge R&R Studies
8.6.4 Practical Task: ANOVA Output for Gauge R&R Studies

8.7 Gauge Linearity and Bias Assessment

8.7.1 Core Principles
8.7.2 Gauge Linearity
8.7.3 Gauge Bias
8.7.4 Assessment: Gauge Linearity and Bias Assessment
8.7.5 Minitab Feature: Gauge Linearity and Bias Study
8.7.6 Practical Task: Gauge Linearity and Bias Assessment

8.8 Attribute Consistency Analysis

8.8.1 Core Principles
8.8.2 Binary Data
8.8.3 Nominal Data
8.8.4 Ordinal Data
8.8.5 Assessment: Attribute Consistency Analysis
8.8.6 Minitab Feature: Attribute Consistency with Binary Data
8.8.7 Minitab Feature: Attribute Consistency with Nominal Data
8.8.8 Minitab Feature: Attribute Consistency with Ordinal Data
8.8.9 Practical Task: Attribute Consistency Analysis

8.9 Summary

8.9.1 Objective Recap

Chapter 9: Experimental Design

9.1 Introduction and Learning Objectives

9.2 Factorial Experimental Designs

9.2.1 Core Principles
9.2.2 Constructing Full Factorial Designs
9.2.3 Evaluating Full Factorial Designs
9.2.4 Assessment: Factorial Experimental Designs
9.2.5 Minitab Feature: Generate Full Factorial Design
9.2.6 Minitab Feature: Evaluate Full Factorial Design
9.2.7 Practical Task: Generate Full Factorial Design
9.2.8 Practical Task: Evaluate Full Factorial Design

9.3 Blocking and Center Points

9.3.1 Blocking Strategies
9.3.2 Center Points
9.3.3 Evaluating Designs with Blocks and Center Points
9.3.4 Assessment: Blocking and Center Points
9.3.5 Minitab Feature: Generate Factorial Design with Blocks and Center Points
9.3.6 Minitab Feature: Evaluate Factorial Design with Blocks and Center Points
9.3.7 Practical Task: Generate Factorial Design with Blocks and Center Points
9.3.8 Practical Task: Evaluate Factorial Design with Blocks and Center Points

9.4 Fractional Factorial Designs

9.4.1 Core Principles
9.4.2 Constructing Fractional Factorial Designs
9.4.3 Evaluating Fractional Factorial Designs
9.4.4 Assessment: Fractional Factorial Designs
9.4.5 Minitab Feature: Generate Fractional Factorial Design
9.4.6 Minitab Feature: Evaluate Fractional Factorial Design

9.5 Optimizing Responses

9.5.1 Response Optimization
9.5.2 Assessment: Response Optimization
9.5.3 Minitab Feature: Response Optimization
9.5.4 Practical Task: Response Optimization

9.6 Wrap-up and Objective Recap