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# Engineering Quality in Product Development

Engineering Quality in Product Development Skill Building Program

Purpose- The purpose of this program is to enhance the quality engineering skills of the practicing product/process development engineer, formulator, scientist, quality engineer, technician and quality professional

The quality engineering skills include the experimental and analytic tools, and best practices used for the design, modification and root cause analysis of products and manufacturing processes. Participation is not limited to the members of the Quality organization since engineering quality into products and processes is the shared responsibility of all involved.

Content consists of the following modular building blocks:

• Basic Statistics & Graphical Data Analysis
• Inferential Statistics, Sample Size & ANOVA
• Capability
• Design of Experiments
• Mixtures Experiments

## Module 1 :Basic Statistics and Graphical Data Analysis

Description:

This session focuses on the techniques necessary to describe data statistically and graphically. Topics include; random variables, types of data, sample statistics, population parameters, central tendency, variation, histograms, distributions, probability, z-scores, standard error and confidence intervals. Important sampling distributions, including the, t, F, Weibull, are reviewed. This session is a prerequisite for the subsequent skill building sessions.

After completing this session the attendee will be able to;

Understand basic and descriptive statistics used to extract information from data

Use Minitab to perform various graphical analyses to extract information from data

Understand how to obtain “better” data allowing greater information content

Understand key sampling distributions related to inferential statistics and DOE

Intended Audience: Anyone whose work requires the analysis of data including research scientists, development, process, quality and test engineers, and marketing.

Prerequisites: Ability to use Minitab

## Module 2: Inferential Statistics, Sample Size and Analysis of Variance (ANOVA)

Description:

This session focuses on the statistical analysis techniques used to detect differences in data. These techniques help to answer questions such as, “Has a design change improved the performance of my design?” “Do suppliers A and B supply the same part with the same performance?” Sample size selection is also considered in terms of the significant difference to be detected and the risk of not being able to detect this difference. Finally, ANOVA is studied to understand this important statistical analytic technique used in measurement system analysis, regression and design of experiments.

Topics include; risk, hypothesis testing, 1 and 2 sample t-test, p-values, power and sample size, test for normality and equal variance, non-parametric statistics, ANOVA.

This session is a prerequisite for the subsequent skill building sessions.

After completing this session the attendee will be able to;

Understand the four risks inherent in any investigation

Use Minitab to interpret p-values

Use Minitab to choose the proper sample size

Interpret a ANOVA table to analyze simple experiments

Intended Audience: Anyone concerned with testing and detecting whether a change has occurred in a “before and after” setting including research scientists, development, process, test and quality engineers, and marketing.

Prerequisites: Basic Stats module. Ability to use Minitab

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