Course Description:
This course is designed to help scientists and engineers apply statistical methods used assist decision making in process and product development. Variability must be considered when utilizing data to arrive at conclusions.
This course will cover Basic Statistics and Graphical Methods used to summarize data.
You will learn how to apply Hypothesis Testing methods to determine whether groups are statistically equivalent or not with respect to key process characteristics such as process averages and variability.
The use of confidence intervals when estimating key parameters will be covered.
When planning studies, sample size determination is critical to ensure that study results will be meaningful. Methods to determine appropriate sample sizes for various types of problems will be covered.
Finally, an introduction to Design of Experiments (DOE) is provided. DOE is an extremely efficient method to understand which variables (and interactions) affect key outcomes and allows the development of mathematical models used to optimize process and product performance. The concepts behind DOE are covered along with some effective types of screening experiments. Case studies will also be presented to illustrate the use of the methods.
This highly interactive course will allow participants the opportunity to practice applying statistical methods with various data sets. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively in their process and product development efforts.
Course Information:
Participants are requested to bring a laptop with Minitab Version 17 software installed.
Learning Objectives:
Effectively summarize data and communicate results with basic statistics and graphical techniques
Apply Hypothesis Testing to test whether two or more groups of data are statistically equivalent or not.
Estimate key process parameters with associated confidence intervals to express estimate uncertainty
Determine appropriate sample sizes for estimation and hypothesis testing
Understand key concepts related to Design of Experiments
Apply experiments to determine cause and effect relationships and model process behaviour
Who will Benefit:
Scientists
Product and Process Engineers
Quality Engineers
Personnel involved in product development and validation
This course is designed to help scientists and engineers apply statistical methods used assist decision making in process and product development. Variability must be considered when utilizing data to arrive at conclusions.
This course will cover Basic Statistics and Graphical Methods used to summarize data.
You will learn how to apply Hypothesis Testing methods to determine whether groups are statistically equivalent or not with respect to key process characteristics such as process averages and variability.
The use of confidence intervals when estimating key parameters will be covered.
When planning studies, sample size determination is critical to ensure that study results will be meaningful. Methods to determine appropriate sample sizes for various types of problems will be covered.
Finally, an introduction to Design of Experiments (DOE) is provided. DOE is an extremely efficient method to understand which variables (and interactions) affect key outcomes and allows the development of mathematical models used to optimize process and product performance. The concepts behind DOE are covered along with some effective types of screening experiments. Case studies will also be presented to illustrate the use of the methods.
This highly interactive course will allow participants the opportunity to practice applying statistical methods with various data sets. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively in their process and product development efforts.
Course Information:
Participants are requested to bring a laptop with Minitab Version 17 software installed.
Learning Objectives:
Effectively summarize data and communicate results with basic statistics and graphical techniques
Apply Hypothesis Testing to test whether two or more groups of data are statistically equivalent or not.
Estimate key process parameters with associated confidence intervals to express estimate uncertainty
Determine appropriate sample sizes for estimation and hypothesis testing
Understand key concepts related to Design of Experiments
Apply experiments to determine cause and effect relationships and model process behaviour
Who will Benefit:
Scientists
Product and Process Engineers
Quality Engineers
Personnel involved in product development and validation
