Minitab Manufacturing
Level 1 Manufacturing Course with Minitab
Length of training: 2 days
Day 1: Introduction to Minitab
Decrease the time required for statistical analysis by quickly learning to navigate Minitab's user-friendly and customizable environment. Learn how to import/export data and output between Minitab and various software and database systems. Enhance your ability to create, manipulate, and restructure data. Develop sound statistical approaches to data analysis by learning how to create and interpret a wide variety of graphs and numerical measures useful for quality improvement initiatives. This course focuses on the utilization of these tools as they pertain to applications commonly found in manufacturing, engineering, and business processes.
Topics covered include: Charts, Histograms, Box plots, Dot plots, Scatter plots, Tables, Measures of Location and Variation, ODBC
Target Participants: Professionals who are working in automotive industries, chemical production plants, and other companies involved in manufacturing. The course materials include examples with metrics such as diameters, pressure, and hardness.
Day 2: Basic Statistics
Augment your graphical analysis skills using Minitab's powerful statistical tools. Develop the foundation for important statistical concepts such as hypothesis testing and confidence intervals. By analyzing a variety of real world data sets, can learn how to match the appropriate statistical tool to your own applications and how to correctly interpret statistical output to quickly reveal problems with a process or to show evidence of an improvement. Learn how to explore critical features in your processes through statistical modeling tools that help to uncover and describe relationships between variables. A strong emphasis is placed on making good business decisions based upon the practical application of statistical techniques commonly found in manufacturing, engineering, and research and development endeavors.
Tools Covered Include: t-Tests, Proportion Tests, Tests for Equal Variance, Power and Sample Size, Correlation, Simple Linear and Multiple Regression, ANOVA and GLM.
Target Participants: Professionals who are working in automotive industries, chemical production plants, and other companies involved in manufacturing. The course materials include examples with metrics such as diameters, pressure, and hardness.
Level 2 Manufacturing Course with Minitab
Length of training: 2 days
Day 1: Statistical Quality Analysis
Develop the necessary skills to successfully evaluate and certify manufacturing and engineering measurement systems. Learn the basic fundamentals of statistical process control and how these important quality tools can provide the necessary evidence to improve and control manufacturing processes. Develop the skills to know when and where to use the various types of control charts available in Minitab for your own processes. Learn how to utilize important capability analysis tools, many enhancement in Minitab Release 15, to evaluate your processes relative to internal and customer specifications. The course emphasis is placed on teaching quality tools as they relate to manufacturing processes.
Tools Covered Include: Gage R&R, Destructive Testing, Gage Linearity, Gage Stability, Attribute Agreement, Variables and Attribute Control Charts, Capability Analysis for Normal, Non-normal and Attribute data.
Target Participants: Professionals who are working in automotive industries, chemical production plants, and other companies involved in manufacturing. The course materials include examples with metrics such as diameters, pressure, and hardness.
Day 2: Factorial Designs
Learn to generate a variety of full and fractional factorial designs using Minitab's intuitive DOE interface. Real-world applications demonstrate how the concepts of randomization, replication, and blocking form the basis for sound experimentation practices. Develop the skills necessary to correctly analyze resulting data to effectively and efficiently reach experimental objectives. Use Minitab's customizable and powerful graphical displays to interpret and communicate experimental results to improve products and processes, find critical factors that impact important response variables, reduce process variation, and expedite research and development projects.
Tools and topics Covered Include: Design of Factorial and Experiments; Normal Effects Plot and Pareto of Effects; Power and Sample Size; Main Effect, Interaction, and Cube Plots; Center Points; Overlaid Contour Plots; Multiple Response Optimization.
Target Participants: Professionals who are working in automotive industries, chemical production plants, and other companies involved in manufacturing. The course materials include examples with metrics such as diameters, pressure, and hardness.
Level 3 Manufacturing Course with Minitab
Length of training: 2 days
Day 1: Advanced Regression and ANOVA
Continue to build on the fundamental statistical analysis concepts taught in the Basis Statistics course by learning additional statistical modeling tools that help to uncover and describe relationship between variables.Hands-on examples illuminate how modeling tools help reveal key inputs and sources variation in your processess.Learn how to use statistical models to investigate how processess may behave under varying conditions.This course provides techniques to help you better understand your processess and to focus and verify your improvement efforts.
Topics Covered Include: Multiple and Stepwise Regression; GLM with Covariates, Nesting and Random Factors; MANOVA; Binary and Nominal Logistic Regression.
Target Participants: Professionals who are working in automotive industries, chemical production plants, and other companies involved in manufacturing. The course materials include examples with metrics such as diameters, pressure, and hardness.
Day 2: DOE in Practice
Learn how to handle common DOE scenarios where classic factorial or response surface design and analysis techniques are neither appropriate nor possible due to the nature of the response variable or the data collection process. Develop techniques for experimental situations often encountered in practice such as missing data and hard-to-change factors. Understand how to account for variables (covariates) that may affect the response but cannot be controlled in the experiment. Explore the importance of minimizing process costs while simultaneously optimizing an important process characteristic. Learn how to find and quantify the effect that factors have on the probability of a critical event, such as a defect, occurring.
Topics and Tools Covered Include: ANCOVA, Unbalanced Designs, Split-Plot Designs, Multiple Response Optimization, Binary Logistic Regression.
Target Participants: Professionals who are working in automotive industries, chemical production plants, and other companies involved in manufacturing. The course materials include examples with metrics such as diameters, pressure, and hardness.