Course Outline
Chapter 1: Descriptive Statistics and Graphical Analysis
Overview
- Learning Outcomes
- Data Classifications
Fundamental Concepts
- Data Categories
- Assessment: Data Categories
Graphical Methods for Data Analysis
- Fundamental Concepts
- Bar Charts and Pareto Charts
- Pie Charts
- Histograms
- Dotplots
- Individual Value Plots
- Boxplots
- Time Series Plots
- Assessment: Graphical Data Analysis
- Minitab Tools: Bar Chart
- Minitab Tools: Pie Chart
- Minitab Tools: Histogram
- Minitab Tools: Dotplot
- Minitab Tools: Individual Value Plot
- Minitab Tools: Boxplot
- Minitab Tools: Time Series Plot
- Practical Application: Graphical Analysis
Statistical Methods for Data Analysis
- Fundamental Concepts
- Mean and Median
- Range, Variance, and Standard Deviation
- Assessment: Statistical Data Analysis
- Minitab Tools: Display Descriptive Statistics
- Practical Application: Descriptive Statistics
Chapter Summary and Objectives Review
Chapter 2: Statistical Inference
2.1 Overview
2.1.1 Learning Outcomes
2.2 Principles of Statistical Inference
2.2.1 Fundamental Concepts
2.2.2 Random Sampling
2.2.3 Assessment: Principles of Statistical Inference
2.2.4 Minitab Tools: Random Sampling
2.3 Sampling Distributions
2.3.1 Fundamental Concepts
2.3.2 Sampling Distribution of the Mean
2.3.3 Assessment: Sampling Distributions
2.4 The Normal Distribution
2.4.1 Fundamental Concepts
2.4.2 Probabilities within a Normal Distribution
2.4.3 Probabilities for the Sample Mean
2.4.4 Assessment: Normal Distribution
2.4.5 Minitab Tools: Cumulative Probabilities with a Normal Distribution
2.4.6 Practical Application: Probabilities and Normal Distributions
2.5 Summary
2.5.1 Objectives Review
Chapter 3: Hypothesis Tests and Confidence Intervals
3.1 Overview
3.1.1 Learning Outcomes
3.2 Tests and Confidence Intervals
3.2.1 Confidence Intervals
3.2.2 Hypothesis Testing
3.2.3 Decision-Making Using Hypothesis Testing
3.2.4 Type I and Type II Errors and Statistical Power
3.2.5 Assessment: Tests and Confidence Intervals
3.3 One-Sample t-Test
3.3.1 Fundamental Concepts
3.3.2 Individual Value Plots
3.3.3 One-Sample t-Test Results
3.3.4 Assumptions
3.3.5 Assessment: One-Sample t-Test
3.3.6 Minitab Tools: One-Sample t-Test
3.3.7 Practical Application: One-Sample t-Test
3.4 Test for Variances
3.4.1 Fundamental Concepts
3.4.2 Boxplots
3.4.3 Two Variances Test Results
3.4.4 Assumptions
3.4.5 Assessment: Two Variances Test
3.4.6 Minitab Tools: Two Variances Test
3.4.7 Practical Application: Two Variances Test
3.5 Two-Sample t-Test
3.5.1 Fundamental Concepts
3.5.2 Individual Value Plots
3.5.3 Two-Sample t-Test Results
3.5.4 Assumptions
3.5.5 Assessment: Two-Sample t-Test
3.5.6 Minitab Tools: Two-Sample t-Test
3.5.7 Practical Application: Two-Sample t-Test
3.6 Paired t-Test
3.6.1 Fundamental Concepts
3.6.2 Individual Value Plots
3.6.3 Paired t-Test Results
3.6.4 Assumptions
3.6.5 Assessment: Paired t-Test
3.6.6 Minitab Tools: Paired t-Test
3.6.7 Practical Application: Paired t-Test
3.7 Test for Proportions
3.7.1 Fundamental Concepts
3.7.2 One Proportion Test Results
3.7.3 Assumptions
3.7.4 Assessment: One Proportion Test
3.7.5 Minitab Tools: One Proportion Test
3.7.6 Practical Application: One Proportion Test
3.8 Test for Two Proportions
3.8.1 Fundamental Concepts
3.8.2 Two Proportions Test Results
3.8.3 Assumptions
3.8.4 Assessment: Two Proportions Test
3.8.5 Minitab Tools: Two Proportions Test
3.8.6 Practical Application: Two Proportions Test
3.9 Chi-Square Test
3.9.1 Fundamental Concepts
3.9.2 Chi-Square Test Results
3.9.3 Assumptions
3.9.4 Assessment: Chi-Square Test
3.9.5 Minitab Tools: Chi-Square Test
3.9.6 Practical Application: Chi-Square Test
3.10 Summary
3.10.1 Objectives Review
Chapter 4: Control Charts
4.1 Overview
4.1.1 Learning Outcomes
4.2 Statistical Process Control
4.2.1 Fundamental Concepts
4.2.2 Patterns in Control Charts
4.2.3 Assessment: Statistical Process Control
4.3 Control Charts for Variable Data in Subgroups
4.3.1 Fundamental Concepts
4.3.2 R Charts
4.3.3 S Charts
4.3.4 Xbar Charts
4.3.5 Assessment: Control Charts for Variable Data in Subgroups
4.3.6 Minitab Tools: Xbar-R Chart
4.3.7 Practical Application: Xbar-R Chart
4.4 Control Charts for Individual Observations
4.4.1 Fundamental Concepts
4.4.2 Moving Range Charts
4.4.3 Individuals Charts
4.4.4 Assessment: Control Charts for Individual Observations
4.4.5 Minitab Tools: I-MR Chart
4.4.6 Practical Application: I-MR Chart
4.5 Control Charts for Attribute Data
4.5.1 Fundamental Concepts
4.5.2 NP and P Charts
4.5.3 C and U Charts
4.5.4 Assessment: Control Charts for Attribute Data
4.5.5 Minitab Tools: P Chart
4.5.6 Practical Application: P Chart
4.6 Summary and Objectives Review
Chapter 5: Process Capability
5.1 Overview
5.1.1 Learning Outcomes
5.2 Process Capability for Normal Data
5.2.1 Fundamental Concepts
5.2.2 Assumptions
5.2.3 Testing for Normality
5.2.4 Assessment: Process Capability for Normal Data
5.2.5 Minitab Tools: Normality Test
5.2.6 Practical Application: Assumptions for Process Capability
5.3 Capability Indices
5.3.1 Potential Capability: Cp and Cpk
5.3.2 Process Performance: Pp and Ppk
5.3.3 Sigma Level
5.3.4 Assessment: Capability Indices
5.3.5 Minitab Tools: Cp and Pp
5.3.6 Minitab Tools: Sigma Level
5.3.7 Practical Application: Process Capability for Normal Data
5.4 Process Capability for Nonnormal Data
5.4.1 Transformations and Alternate Distributions
5.4.2 Box-Cox Transformation
5.4.3 Johnson Transformation
5.4.4 Alternate Distributions
5.4.5 Assessment: Process Capability for Nonnormal Data
5.4.6 Minitab Tools: Box-Cox Transformation
5.4.7 Minitab Tools: Johnson Transformation
5.4.8 Minitab Tools: Capability Analysis with Johnson Transformation
5.4.9 Minitab Tools: Alternate Distributions
5.4.10 Minitab Tools: Capability Analysis with Alternate Distributions
5.4.11 Practical Application: Process Capability with Data Transformations
5.4.12 Practical Application: Process Capability with Alternate Distributions
5.5 Summary
5.5.1 Objectives Review
Chapter 6: Analysis of Variance (ANOVA)
6.1 Overview and Learning Outcomes
6.2 Fundamentals of ANOVA
6.2.1 Fundamental Concepts
6.2.2 Graphs and Summary Statistics
6.2.3 Assessment: Fundamentals of ANOVA
6.3 One-Way ANOVA
6.3.1 Hypothesis Tests
6.3.2 F-Statistics and P-Values
6.3.3 Multiple Comparisons
6.3.4 Assumptions and Residual Plots
6.3.5 Assessment: One-Way ANOVA
6.3.6 Minitab Tools: One-Way ANOVA
6.3.7 Practical Application: One-Way ANOVA
6.4 Two-Way ANOVA
6.4.1 Fundamental Concepts
6.4.2 Graphs
6.4.3 Hypothesis Tests
6.4.4 F-Statistics and P-Values
6.4.5 Assumptions and Residual Plots
6.4.6 Assessment: Two-Way ANOVA
6.4.7 Minitab Tools: Two-Way ANOVA
6.4.8 Practical Application: Two-Way ANOVA
6.5 Summary
Chapter 7: Correlation and Regression
7.1 Overview
7.1.1 Learning Outcomes
7.2 Relationship Between Two Quantitative Variables
7.2.1 Fundamental Concepts
7.2.2 Scatterplot
7.2.3 Correlation
7.2.4 Assessment: Relationship Between Two Quantitative Variables
7.2.5 Minitab Tools: Scatterplot
7.2.6 Minitab Tools: Correlation
7.2.7 Practical Application: Scatterplots and Correlation
7.3 Simple Regression
7.3.1 Fundamental Concepts
7.3.2 Regression
7.3.3 Hypothesis Tests and R-squared
7.3.4 Assumptions and Residual Plots
7.3.5 Assessment: Simple Regression
7.3.6 Minitab Tools: Simple Regression
7.3.7 Practical Application: Simple Regression
7.4 Summary and Objectives Review
Chapter 8: Measurement Systems Analysis
8.1 Overview
8.1.1 Learning Outcomes
8.2 Fundamentals of Measurement Systems Analysis
8.2.1 Fundamental Concepts
8.2.2 Accuracy
8.2.3 Precision
8.2.4 Comparing Accuracy and Precision
8.2.5 Assessment: Fundamentals of Measurement Systems Analysis
8.3 Repeatability and Reproducibility
8.3.1 Fundamental Concepts
8.3.2 Gage R&R Studies
8.3.3 Assessment: Repeatability and Reproducibility
8.4 Graphical Analysis of a Gage R&R Study
8.4.1 Fundamental Concepts
8.4.2 Components of Variation
8.4.3 Xbar and R Charts
8.4.4 Interaction between Operator and Part
8.4.5 Comparative Plots
8.4.6 Gage Run Charts
8.4.7 Assessment: Graphical Analysis of a Gage R&R Study
8.4.8 Minitab Tools: Crossed Gage R&R Study
8.4.9 Minitab Tools: Gage Run Chart
8.4.10 Practical Application: Graphical Analysis of a Gage R&R Study
8.5 Variation
8.5.1 Standard Deviation and Study Variation
8.5.2 Tolerance
8.5.3 Process Variation
8.5.4 Assessment: Variation
8.5.5 Practical Application: Numerical Analysis of a Gage R&R Study
8.6 ANOVA with a Gage R&R Study
8.6.1 Variance Components
8.6.2 Analysis of Variance Tables
8.6.3 Assessment: ANOVA with a Gage R&R Study
8.6.4 Practical Application: ANOVA Output for a Gage R&R Study
8.7 Gage Linearity and Bias Study
8.7.1 Fundamental Concepts
8.7.2 Gage Linearity
8.7.3 Gage Bias
8.7.4 Assessment: Gage Linearity and Bias Study
8.7.5 Minitab Tools: Gage Linearity and Bias Study
8.7.6 Practical Application: Gage Linearity and Bias Study
8.8 Attribute Agreement Analysis
8.8.1 Fundamental Concepts
8.8.2 Binary Data
8.8.3 Nominal Data
8.8.4 Ordinal Data
8.8.5 Assessment: Attribute Agreement Analysis
8.8.6 Minitab Tools: Attribute Agreement Analysis with Binary Data
8.8.7 Minitab Tools: Attribute Agreement Analysis with Nominal Data
8.8.8 Minitab Tools: Attribute Agreement Analysis with Ordinal Data
8.8.9 Practical Application: Attribute Agreement Analysis
8.9 Summary
8.9.1 Objectives Review
Chapter 9: Design of Experiments
9.1 Overview and Learning Outcomes
9.2 Factorial Designs
9.2.1 Fundamental Concepts
9.2.2 Creating Full Factorial Designs
9.2.3 Analyzing Full Factorial Designs
9.2.4 Assessment: Factorial Designs
9.2.5 Minitab Tools: Create a Full Factorial Design
9.2.6 Minitab Tools: Analyze a Full Factorial Design
9.2.7 Practical Application: Create a Full Factorial Design
9.2.8 Practical Application: Analyze a Full Factorial Design
9.3 Blocking and Incorporating Center Points
9.3.1 Blocking
9.3.2 Center Points
9.3.3 Analyzing Designs with Blocks and Center Points
9.3.4 Assessment: Blocking and Incorporating Center Points
9.3.5 Minitab Tools: Create a Factorial Design with Blocks and Center Points
9.3.6 Minitab Tools: Analyze a Factorial Design with Blocks and Center Points
9.3.7 Practical Application: Create a Factorial Design with Blocks and Center Points
9.3.8 Practical Application: Analyze a Factorial Design with Blocks and Center Points
9.4 Fractional Factorial Designs
9.4.1 Fundamental Concepts
9.4.2 Creating Fractional Factorial Designs
9.4.3 Analyzing Fractional Factorial Designs
9.4.4 Assessment: Fractional Factorial Designs
9.4.5 Minitab Tools: Create a Fractional Factorial Design
9.4.6 Minitab Tools: Analyze a Fractional Factorial Design
9.5 Response Optimization
9.5.1 Response Optimization
9.5.2 Assessment: Response Optimization
9.5.3 Minitab Tools: Response Optimization
9.5.4 Practical Application: Response Optimization
9.6 Summary and Objectives Review
Requirements
Testimonials (7)
I liked the excercises and how great it was to follow
Elizabeth Seed - Terumo Aortic
Course - Minitab for Statistical Data Analysis
Going through all the different examples and explaining each of the terms.
Lewis Print - Terumo Aortic
Course - Minitab for Statistical Data Analysis
Interactive, well explained and not overly in-depth with each section required to cover each section
Christopher Beattie - Terumo Aortic
Course - Minitab for Statistical Data Analysis
The practical demonstrations
Simson McCreath - Terumo Aortic
Course - Minitab for Statistical Data Analysis
The trainer had an excellent understanding of the subject matter, and was able to answer any questions easily and concisely.
Craig Renfrew - Terumo Aortic
Course - Minitab for Statistical Data Analysis
Gaining practical experience of Minitab software.
Layna Thompson - Terumo Aortic
Course - Minitab for Statistical Data Analysis
Trainer was very knowledgeable
