Course Descriptions

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, Boxplots, Dotplots, Scatterplots, Tables, Measures of Location and Variation, ODBC

This course is a prerequisite for all other general Minitab courses.

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, 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

Prerequisite: Introduction to Minitab

Optional Topic for On-Site Training: Nonparametric Tests

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 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 and Bias, Attribute Agreement, Variables and Attribute Control Charts, Capability Analysis for Normal, Non-normal and Attribute data

Prerequisites: Introduction to Minitab and Basic Statistics

Optional Topic for On-Site Training: Acceptance Sampling for Attribute and/or Variables Data

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 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

Prerequisites: Introduction to Minitab and Basic Statistics

Continue to build on the fundamental statistical analysis concepts taught in the Basic Statistics course by learning additional statistical modeling tools that help to uncover and describe relationships between variables. Hands-on examples illuminate how modeling tools help reveal key inputs and sources of variation in your processes. Learn how to use statistical models to investigate how processes may behave under varying conditions. This course provides techniques to help you better understand your processes 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

Prerequisites: Introduction to Minitab and Basic Statistics

Expand your knowledge of basic 2 level full and fractional factorial designs to those that are ideal for process optimization. Learn how to use Minitab’s DOE interface to create response surface designs, analyze experimental results, and find optimal factor settings. Learn how to experiment in the real world by using techniques such as sequential experimentation that balance the discovery of critical process information while being sensitive to the resources required to obtain that information. Learn how to find factor settings that simultaneously optimize multiple responses.

Topics Covered Include: Central Composite and Box-Behnken Designs, Calculations for Steepest Ascent, Overlaid Contour Plots, Multiple Response Optimization

Prerequisites: Introduction to Minitab, Basic Statistics, and Factorial Designs

Learn the principles of designing experiments and analyzing the resulting data for processes that are comprised of the mixing and blending of ingredients such as those commonly found in the chemical, food, and beverage industries. By utilizing Minitab’s easy to understand interface, create experiments designed to study and uncover important process information related to mixture processes with the minimal amount of experimental resources. Learn how to interpret graphical and statistical output to understand a mixture’s blending properties and to choose the appropriate mixture of ingredients needed to optimize one or more critical process characteristics.

Tools and Topics Covered Include: Simplex Lattice and Centroid Designs, Upper and Lower Constraints, Extreme Vertices Designs, Pseudocomponents, Response Trace Plots, Mixtures with Process Variables, Mixture Amounts

Prerequisites: Introduction to Minitab, Basic Statistics, and Factorial Designs

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 opportunities to minimize costs or variability while simultaneously optimizing an important product or 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

Prerequisites: Introduction to Minitab, Basic Statistics, and Factorial Designs

Optional Topic for On-Site Training: Taguchi Designs

Determine lifetime characteristics of a product using both graphical and quantitative analysis methods. Examine case studies containing censored and uncensored data to learn how to correctly handle a wide variety of data structures commonly found in reliability. Explore the common distributions used to model failure rates and develop necessary skills in choosing these models.

Tools covered include: Parametric and Nonparametric Distribution Analysis, Estimation and Demonstration Test Plans, Growth Curves, Multiple Failure Modes, Warranty Predictions, and Weibayes Analysis.

Prerequisites: Introduction to Minitab and Basic Statistics

Study and describe the impact that explanatory variables have on product lifetime. Determine the effect of factors and covariates on product failure and the risk of failure to a population of products. Learn how to obtain reliability estimates on highly reliable products in a reasonable amount of time and assess when those components are expected to fail. Establish appropriate sample sizes and allocation of units to stress levels for an accelerated life test, and determine the effect of a stress variable on the probability of failure. A strong emphasis is placed on using appropriate probability models to predict important lifetime characteristics of your products once in the field.

Tools covered include: Probit Analysis, Regression with Life Data, Accelerated Life Testing and Test Plans.

Prerequisites: Introduction to Minitab, Basic Statistics, and Introduction to Reliability

This workshop teaches hands-on macro writing using instructor-led, relevant scenarios. Become more efficient by automating common data analysis tasks, such as combining several different analyses to execute at once. Make Minitab analyses more accessible by writing macros that automatically acquire specified data from a database and perform statistical analysis with minimal user input. Students will learn in this intense one-day workshop how to write macros that can import data from poorly structured Excel files, such as data files from Coordinate Measuring Machines (CMM), and restructure them for analysis in Minitab. It will be taught by an experienced Minitab instructor with years of engineering experience to ensure students will be able to transfer what they learn in the classroom back at work immediately.

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 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 business, transactional, and service industries.

Topics Covered Include: Pareto Charts, Time Series plots, Individual value plots, Bar charts, Histograms, Boxplots, Dotplots, Scatterplots, Tables, Measures of Location and Variation, ODBC.

This course is a prerequisite for all other Minitab Service Quality courses.

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, learn how to match the appropriate statistical tool to your own applications and how to correctly interpret statistical output to reveal problems quickly 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. The course emphasis is on making good business decisions through the use of statistical tools commonly used in business, transactional, and service processes.

Tools Covered Include: t-Tests, Proportion Tests, Tests for Equal Variance, Power and Sample Size, Tables and Chi-Square Analysis, Correlation, Simple Linear Regression, ANOVA

Prerequisite: Introduction to Minitab (Service Quality)

Optional Topic for On-Site Training: Nonparametric Tests

Develop the necessary skills to successfully evaluate and certify your 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 your processes. Develop the skills to know when and where to use the various types of control charts available in Minitab. Learn how to utilize important capability analysis tools to evaluate your processes relative to internal and customer specifications. The course emphasizes the teaching of quality tools as they pertain to service industries.

Tools Covered Include: Attribute Agreement for Binary, Nominal and Ordinal Data; Kappa and Kendall’s Coefficients; Gage R&R; Variables and Attribute Control Charts; Capability Analysis for Normal, Non-normal, and Attribute data.

Prerequisites: Introduction to Minitab (Service Quality) and Basic Statistics (Service Quality)

Optional Topic for On-Site Training: Acceptance Sampling for Attribute and/or Variables Data

Expand your set of available statistical tools by analyzing data from real world problems experienced in service industries. Strengthen analysis skills with tools used to explore and describe relationships between variables. Learn to discover and describe features in data related to the effect and impact of time, and how to forecast future process behavior. Utilize graphical and quantitative approaches to describe similarities and differences between the effects of various factors on important quality characteristics. Learn how to find and quantify the effect that factors have on the probability of a critical event occurring.

Tools Covered Include: GLM; Binary Logistic Regression; Factorial Designs; Time Series Tools Including Exponential Smoothing, Trend Analysis, Decomposition, Multiple Linear Regression including Best Subsets and Stepwise Regression.

Prerequisites: Introduction to Minitab (Service Quality) and Basic Statistics (Service Quality)

In this 2-day course you will quickly learn how to navigate Quality Companion’s user-friendly and customizable environment. Learn how to identify a potential project and quantify its risks. Define and scope a project to more easily gain buy-in from key stakeholders. Learn to use Quality Companion’s built-in Roadmaps and Coaches to determine which tools and statistical analyses are appropriate at any phase of the project. Define a process and manage its activities to gain insight into the value stream. Modify Quality Companion’s built-in tools to reflect your preferred quality improvement methodology. Create custom data fields and categories as well as custom project and tool templates that can be stored as permanent software options and shared with other users. Learn how to use and customize the Quality Companion Dashboard to monitor the progress and results for multiple projects across your organization.

Topics covered include: Quality Companion environment, Project Manager, Roadmap, Coaches, Templates, Custom Data Fields, Project Data Sharing and Modifying Forms.

Tools covered include: Process mapping, Brainstorming / Fishbone diagram, Y metrics, Ballots, Presentations, Analysis Capture tools, Value Stream mapping, and the Quality Companion Dashboard as well as various form tools such as Project Charter, C&E Matrix, FMEA, and more.

Recommendation: View Quality Companion recorded webcasts prior to attending this course.