Dublin, March 31, 2023 (GLOBE NEWSWIRE) -- The "Statistical Analysis for Process and Product Development" training has been added to ResearchAndMarkets.com's offering.
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 Descriptive 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 descriptive 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 Should Attend:
Who will Benefit:
- Scientists
- Product and Process Engineers
- Quality Engineers
- Personnel involved in product development and validation
Key Topics Covered:
- DAY 01(8:30 AM - 2:30 PM)
- 8:30 - 9:00 AM: Registration
- 9:00 AM: Session Start Time
- Descriptive Statistics & Distributions
- Data Types
- Populations & Samples
- Central Tendency and Variation
- Probability Distributions
- The Normal Distribution
- Hypothesis Testing Concepts
- Test Statistics, Crit. Values, p-values
- One and Two Sided Tests
- Type I and Type II Errors
- Estimation and Confidence Intervals
- Hypothesis Tests for One and Two Groups
- Testing Means (1 sample t ,2 sample t and paired t tests)
- Testing Variances (Chi-Square, F test)
- Testing Proportions (overview)
- Tolerance Intervals
- Equivalence Tests
- Hypothesis Tests for Multiple (>2) Groups
- Testing Means (ANOVA)
- Multiple Comparisons
- Testing Variances (Bartlett's and Levene's Test)
- Testing for Normality
DAY 02 (8:30 AM - 2:30 PM)
- Power & Sample Size
- Type II Errors and Power
- Factors affecting Power
- Computing Sample Sizes
- Power Curves
- Sample Sizes for Estimation
- Introduction to Experimental Design
- What is DOE?
- Definitions
- Sequential Experimentation
- When to use DOE
- Common Pitfalls in DOE
- A Guide to Experimentation
- Planning an Experiment
- Implementing an Experiment
- Analyzing an Experiment
- Case Studies
- Two Level Factorial Designs
- Design Matrix and Calculation Matrix
- Calculation of Main & Interaction Effects
- Interpreting Effects
- Using Center Points
- Identifying Significant Effects
- Determining which effects are statistically significant
- Analyzing Replicated and Non-replicated Designs
- Developing Mathematical Models
- Developing First Order Models
- Residuals /Model Validation
- Optimizing Responses
For more information about this training visit https://www.researchandmarkets.com/r/ofugvu
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