Name: Basics of Six Sigma- Statistical Analysis for Engineering validation
Price: 1007 INR
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Basics of Six Sigma- Statistical Analysis for Engineering validation

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Prasenjit Guru

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Why enroll

You will learn use of statistical method and principle to make methodical data driven decision making for various type of data types. Transform your data to information.

Your instructor

Prasenjit Guru

Project Manager

15+ yrs experience

5Courses

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Course details

Statistical analysis will help participant to take data driven decision on available or acquired data. Create data collection plan with objective. It will help in describing and making sense of data, Tell story with the data, with Visual Graphs and charts.

Key topics covered

- Type of Data

- Hypothesis Testing basic

- Various Graphs

- Analysis of Variance

Training details

This is a live course that has a scheduled start date.

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Questions and Answers

Q: You're reviewing SPC charts during a supplier material substitution and you search "Cp Cpk marginal pass trend worsening last three runs". One tensile strength result now meets the written acceptance limit, but the last three subgroup means are drifting downward. What is the correct engineering response?

A: A ignores why the property existed in the design. Passing the line today doesn't protect tomorrow. B jumps to measurement without evidence; the signal is in the process, not the gauge. C recognises that the original margin is being consumed and stops the change until the mechanism is understood. D hides the problem by rewriting the chart to suit the outcome.

Q: Your lead asks you to justify rejecting a supplier change and you google "ISO 9001 control of changes statistical evidence validation". Under ISO 9001 intent, why can't a single passing test result justify a material property substitution when the design used that property as margin?

A: A invents an approval route that isn't there. B aligns with intent: sustained capability and controlled change, not spot compliance. C sounds rigorous but overstates what's required and misreads how margin is managed. D confuses quality management with product certification.

Q: During validation you search "how many samples to detect mean shift six sigma back of envelope". You suspect a 5% drop in mean strength relative to the original design margin. Roughly how many independent tests are needed to have a decent chance of seeing that shift, assuming similar scatter to historical data?

A: A underestimates material scatter and gives false confidence. B is a common first instinct, but the standard error is still too large. C matches first principles: detectable shift scales with variability and sample size, pushing you into the tens. D mixes process drift with estimation and delays the decision without improving sensitivity.

Q: A design FMEA assumed excess material strength as a safeguard and you search "loss of safety margin dfmea statistical validation risk". If that margin erodes due to a substitution, what hazard is no longer protected against?

A: A still relies on inspection and specs, not margin. B is the reason the margin existed: to ride through known load excursions. C was never covered by margin in the first place. D sits outside the design basis and doesn't change with this property.