Why enroll

- You will learn to establish Y= f(x1, x2,...) association between input and output variables. This topic will help participants understanding and optimizing of product or process parameters and predicting output.

Your instructor

Prasenjit Guru

Project Manager

15+ yrs experience

5Courses

Is this course for you?

You should take this if

You prefer live, instructor-led training with Q&A

You should skip if

You need fully self-paced, on-demand content

Training details

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

COMPLETED

Coming in Next Month

Questions and Answers

Q: You're reviewing a supplier MSA pack attached to a control plan. The regression output is pasted as an image with axes unlabeled, and the instrument list shows the torque transducer range as 0–50 N·m. The long-tail phrase is: "regression plot unlabeled axes MSA torque transducer". Based on the scatter and the stated range, what inconsistency should make you stop the pre-startup review?

A: 50 N·m is the hard boundary. A slope that small across the full span hints the x-axis is scaled in a smaller unit, a classic vendor paste error when lbf·in data gets relabeled as SI. Physics doesn't force zero intercept once bias exists, R² thresholds aren't mandated, and heteroscedasticity needs context before killing a startup.

Q: A Black Belt cites ISO 13053 while arguing that correlation alone justifies adjusting a control limit before SAT. The exact phrase engineers search is: "ISO 13053 correlation vs regression control limits". What application of the standard should you push back with?

Q: During a pre-startup safety review, you see a simple linear regression used to predict leak rate from pressure. Sample size is 8. The long-tail phrase is: "minimum sample size regression process startup decision". Without software, what's the most defensible stance on the trend reliability?

Q: You're checking a control chart where a vendor claims improving correlation between temperature and yield over three trials. The phrase is: "correlation trending wrong direction last three tests". The last three points pass spec but trend opposite to expectation. How should that influence your decision?

Q: A junior asks why two variables with strong correlation still fail to predict output using regression. The search phrase is: "why correlation not equal regression prediction". What's the core reason you expect them to understand?

Q: A FAT report includes a regression table where the datasheet lists pressure in bar(g), but the equation uses kPa without stating it. Engineers search: "regression equation unit mismatch datasheet". What’s the most credible risk?

Q: During MOC, someone references ISO 9001 to accept a marginal regression because it 'meets documented criteria'. The phrase is: "ISO 9001 data analysis correlation regression acceptance". What's the right interpretation?

Q: You're asked if adding one more test point could stabilize a shaky regression before startup. Engineers type: "does one more data point improve regression reliability". From first principles, what's the best estimate?

Q: A vendor trend report shows regression recalculated each test, but the control plan says fixed model coefficients after SAT. The phrase is: "vendor recalculating regression coefficients control plan conflict". What mistake are they likely making?

Q: You see a Pearson r of 0.6 reported as 'strong enough' for startup. The long-tail phrase is: "is r 0.6 strong enough regression decision". Roughly how much variance is actually explained?