This track includes data analysis techniques to compare several groups, understand correlation, and model both linear and nonlinear relationships between variables. Analytical and statistical principles will be presented through real-world case studies, examples, and exercises.
This course is most appropriate for business analysts, process engineers, R&D team members, and other quality professionals who need to understand how to build and evaluate statistical models.
- Minitab Essentials
- Statistical Modeling
In this 2-day foundational course you will learn to minimize the time required for data analysis by using Minitab to import data, develop sound statistical approaches to exploring data, create and interpret compelling graphs, and export results. Analyze a variety of real world data sets to learn how to align your applications with the right statistical tool, and interpret statistical output to reveal problems with a process or evidence of an improvement. Learn the fundamentals of important statistical concepts, such as hypothesis testing and confidence intervals, and how to uncover and describe relationships between variables with statistical modeling tools.
This course places a strong emphasis on making sound decisions based upon the practical application of statistical techniques commonly found in manufacturing, engineering, and research and development endeavors.
- Importing and Formatting Data
- Bar Charts
- Pareto Charts
- Tables and Chi-Square Analysis
- Measures of Location and Variation
- Proportion Tests
- Tests for Equal Variance
- Power and Sample Size
- Simple Linear and Multiple Regression
- One-Way ANOVA
- Multi-Variable ANOVA
Continue to build on the fundamental statistical analysis concepts taught in the Minitab Essentials 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.
- Multiple and Stepwise Regression
- Nonlinear Regression
- Partial Least Squares
- Multi-Variable ANOVA with Covariates
- Nesting and Random Factors
- Binary and Nominal Logistic Regression