Column A contains diameter measurements for 200 randomly selected pins with a nominal diameter specification of 10 mm The Continuous Improvement analyst believes the underlying process is best approximated by a Weibull distribution (2 parameter) Assuming the analyst is correct in asserting the underlying process is a 2 parameter Weibull, use Minitab to estimate the shape and scale parameters of the Weibull Find shape parameter (alpha), scale parameter (beta), mean sample standard deviation So, once you find estimates for alpha and beta, try using those values to calculate the expected value for the Weibull distribution There is a handy integral calculator athttps www integral calculator com Using this method, I found an expected value of 10 0813, which is very close the average pin diameter you should calculate for part c, using the sample data 10 12 10 11 10 12 10 03 10 1 9 94 10 09 10 04 10 01 10 04 10 03 10 08 10 04 10 06 10 12 10 11 10 07 10 1 10 08 10 03 10 08 10 04 10 04 10 11 10 10 12 10 11 10 08 10 1 10 1 10 14 10 1 10 14 10 01 10 07 10 1 10 04 10 11 10 13 9 97 10 02 10 07 10 08 10 11 10 08 10 07 10 11 10 12 10 1 10 08 10 11 10 11 10 05 10 04 10 1 10 04 10 13 10 09 10 13 10 07 10 12 10 12 10 01 10 13 10 08 10 08 10 14 10 07 10 07 10 12 10 13 10 13 10 1 10 08 10 08 10 05 10 07 10 08 10 10 06 10 13 10 09 10 06 10 11 10 11 10 12 10 1 10 07 10 07 10 08 10 06 10 1 10 09 10 08 10 06 10 11 10 09 10 03 9 92 10 11 10 08 10 12 10 08 10 13 10 1 10 06 10 12 10 12 10 12 10 07 10 16 10 09 10 11 10 1 9 98 10 12 10 06 10 07 10 08 10 12 10 05 10 09 10 08 10 11 10 15 10 02 10 07 10 08 10 11 10 11 10 08 10 15 10 12 10 1 10 09 10 07 10 09 10 13 10 06 9 99 10 09 10 1 10 06 10 08 10 15 10 06 10 07 10 11 10 08 10 05 10 07 10 07 10 11 10 08 10 10 04 10 07 10 04 10 12 10 11 10 13 10 1 10 14 9 99 10 1 10 15 10 11 10 01 10 06 10 12 10 02 10 13 10 10 09 10 07 10 11 10 06 10 01 10 14 10 16 10 08 10 04 10 08 10 11 10 09 10 09 10 11 10 13 10 09 10 1 10 04 10 1 10 11 10 09 10 11 10 02 10 13 10 12 10 1 10 11

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Column A contains diameter measurements for 200 randomly selected pins with a nominal diameter specification of 10 mm. The Continuous Improvement analyst believes the underlying

Column A contains diameter measurements for 200 randomly selected pins with a nominal diameter specification of 10 mm. The Continuous Improvement analyst believes the underlying process is best approximated by a Weibull distribution (2-parameter). Assuming the analyst is correct in asserting the underlying process is a 2-parameter Weibull, use Minitab to estimate the shape and scale parameters of the Weibull.

Find shape parameter (alpha), scale parameter (beta), mean & sample standard deviation.

So, once you find estimates for alpha and beta, try using those values to calculate the expected value for the Weibull distribution. There is a handy integral calculator athttps://www.integral-calculator.com/. Using this method, I found an expected value of 10.0813, which is very close the average pin diameter you should calculate for part c, using the sample data.

10.12
10.11
10.12
10.03
10.1
9.94
10.09
10.04
10.01
10.04
10.03
10.08
10.04
10.06
10.12
10.11
10.07
10.1
10.08
10.03
10.08
10.04
10.04
10.11
10
10.12
10.11
10.08
10.1
10.1
10.14
10.1
10.14
10.01
10.07
10.1
10.04
10.11
10.13
9.97
10.02
10.07
10.08
10.11
10.08
10.07
10.11
10.12
10.1
10.08
10.11
10.11
10.05
10.04
10.1
10.04
10.13
10.09
10.13
10.07
10.12
10.12
10.01
10.13
10.08
10.08
10.14
10.07
10.07
10.12
10.13
10.13
10.1
10.08
10.08
10.05
10.07
10.08
10
10.06
10.13
10.09
10.06
10.11
10.11
10.12
10.1
10.07
10.07
10.08
10.06
10.1
10.09
10.08
10.06
10.11
10.09
10.03
9.92
10.11
10.08
10.12
10.08
10.13
10.1
10.06
10.12
10.12
10.12
10.07
10.16
10.09
10.11
10.1
9.98
10.12
10.06
10.07
10.08
10.12
10.05
10.09
10.08
10.11
10.15
10.02
10.07
10.08
10.11
10.11
10.08
10.15
10.12
10.1
10.09
10.07
10.09
10.13
10.06
9.99
10.09
10.1
10.06
10.08
10.15
10.06
10.07
10.11
10.08
10.05
10.07
10.07
10.11
10.08
10
10.04
10.07
10.04
10.12
10.11
10.13
10.1
10.14
9.99
10.1
10.15
10.11
10.01
10.06
10.12
10.02
10.13
10
10.09
10.07
10.11
10.06
10.01
10.14
10.16
10.08
10.04
10.08
10.11
10.09
10.09
10.11
10.13
10.09
10.1
10.04
10.1
10.11
10.09
10.11
10.02
10.13
10.12
10.1
10.11