Mathews Malnar and
Bailey, Inc. Quality engineering, applied statistical consulting, and training services for R&D, product, process, and manufacturing engineering organizations. |

An Introduction to MINITAB

Course Description: This hands-on course provides an introduction to statistical methods for quality engineering using MINITAB. Students will learn to enter and manage their data in MINITAB worksheets, create graphical displays of their data, perform basic statistical analyses like confidence intervals, hypothesis tests, ANOVA, and linear regression, and use special methods like designed experiments, gage error studies, process capability studies, and sample size calculations.

Course Format: This course is usually taught in three four-hour sessions with a possible fourth session dedicated to specific customer applications. The course must be taught in a computer lab with one computer per student.

Course Goals: Upon completion of this course students should be able to use MINITAB to:

1. Enter data
into worksheets manually, by copy/paste operations, from Excel
spreadsheets, and from text files.

2. Manipulate data and make simple calculations.

3. Calculate descriptive statistics for sample data.

4. Calculate probabilities and inverse probabilities for common probability distributions.

5. Create and edit basic graphical data presentations.

6. Construct and interpret normal probability plots.

7. Construct confidence intervals and perform simple hypothesis tests for one and two sample location and variation problems.

8. Perform hypothesis tests for one and two sample fractions defective.

9. Design and use acceptance sampling plans for attribute and variable data.

10. Create control charts for defectives, defects, and measurement data.

11. Fit lines and curves to data and plot them.

12. Use ANOVA to test one- and two-way classification problems for differences between treatments.

13. Create and analyze two-level factorial designed experiments.

14. Design gage error studies and analyze their data.

15. Analyze data from process capability studies.

16. Calculate sample sizes for experiments.

17. Create reports including data, statistical analyses, and graphs.

18. Store work in project and worksheet files.

19. Write and execute simple macros.

2. Manipulate data and make simple calculations.

3. Calculate descriptive statistics for sample data.

4. Calculate probabilities and inverse probabilities for common probability distributions.

5. Create and edit basic graphical data presentations.

6. Construct and interpret normal probability plots.

7. Construct confidence intervals and perform simple hypothesis tests for one and two sample location and variation problems.

8. Perform hypothesis tests for one and two sample fractions defective.

9. Design and use acceptance sampling plans for attribute and variable data.

10. Create control charts for defectives, defects, and measurement data.

11. Fit lines and curves to data and plot them.

12. Use ANOVA to test one- and two-way classification problems for differences between treatments.

13. Create and analyze two-level factorial designed experiments.

14. Design gage error studies and analyze their data.

15. Analyze data from process capability studies.

16. Calculate sample sizes for experiments.

17. Create reports including data, statistical analyses, and graphs.

18. Store work in project and worksheet files.

19. Write and execute simple macros.

Prerequisites: This course is intended for students who have practical knowledge of and experience with statistical quality engineering methods but have no or limited experience with MINITAB. Students should have successfully completed a short course in basic statistical methods including:

- Basic graphical data presentation techniques
- Calculation of statistics for location (e.g. the mean) and variation (e.g. the standard deviation)
- An introduction to attribute probability distributions for defectives (hypergeometric and binomial) and defects (Poisson)
- An introduction to the design and operation of acceptance sampling methods for defects and defectives
- An introduction to the use of the normal distribution to describe variables data
- An introduction to confidence intervals for population parameters
- An introduction to hypothesis tests for location and variation
- An introduction to acceptance sampling for attributes and
variables

- An introduction to SPC control charts for defectives, defects, and measurement data
- An introduction to linear regression methods
- An introduction to ANOVA

Course Outline: Each section includes a short presentation and one or more hands-on exercises:

1. Overview
of the MINITAB Environment

2. Data Types and Format

3. Entering and Saving Data

4. Manipulating Data

5. Calculations

6. Descriptive Statistics

7. Graphs

8. Calculating Probabilities

9. Confidence Intervals for Attribute and Variables Data

10. Tests for Attribute and Variables Data

11. Acceptance Sampling

12. Statistical Process Control

13. Analysis of Variance

14. Linear Regression

15. Design of Experiments

16. Gage Error Studies

17. Process Capability Studies

18. Reliability/Life Test Data Analysis

19. Sample Size and Power Calculations

20. Command Line Operations

21. MINITAB Macros

2. Data Types and Format

3. Entering and Saving Data

4. Manipulating Data

5. Calculations

6. Descriptive Statistics

7. Graphs

8. Calculating Probabilities

9. Confidence Intervals for Attribute and Variables Data

10. Tests for Attribute and Variables Data

11. Acceptance Sampling

12. Statistical Process Control

13. Analysis of Variance

14. Linear Regression

15. Design of Experiments

16. Gage Error Studies

17. Process Capability Studies

18. Reliability/Life Test Data Analysis

19. Sample Size and Power Calculations

20. Command Line Operations

21. MINITAB Macros

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