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What is the baseline z-score?

A Six Sigma Black Belt has compiled the following information regarding computer chips for a DMAIC project and wants to determine the baseline sigma score in the MEASURE phase.

- There a 7 defect types in each computer chip
- 17,500 computer chips were analyzed, 10,650 had one or more defects
- 19,207 total defects were found among the 17,500 chips

DPU = 19,207 / 17,500 = 1.0975

DPO = 19,207 / (17,500 * 7) = 0.156792

DPMO = DPO * 1,000,000 = 156,792

TPY = 1 - DPO = 1 - 0.156792 = 0.843208 = 84.3208%

Z-score (sigma score) = NORM.S.INV(TPY) + 1.5 (sigma shift) = NORM.S.INV(0.843208) + 1.5 = 1.0077 + 1.5 = 2.5077

**Calculate the sample size required for a discrete (binomial) proportion estimation with an unknown population. Given **

z = 1.645 for a confidence level (α) of 90%.

p-bar = 0.5

E = 2% = 0.02

Therefore, solving for the sample size

n = 1.6452 * 0.5 * (1 - 0.5) / 0.022

n = 0.6765062 / 0.0004 = 1691.27 (always round up)

n = 1692. A minimum of 1,692 samples are needed.

If you wanted a confidence level of 95%, the z-score is 1.96 and n becomes 2,401 samples.

Click here to learn more about calculating sample sizes for various types of data.

**A common use of the student t-test is to detect a difference (or not) in **

1) Means

2) Variation

3) Degrees of Freedom

4) Power

Answer: Visit T-Test Module for the answer and more insight

**To further test your Lean Six Sigma knowledge and prepare for your certification exam, you will find 180+ questions by ****clicking here****. **

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A goal of the Six Sigma project manager, in the MEASURE phase, is to recognize potential bias in the data (or responses) and prevent it from coming into play if possible. Forms of bias that can be introduced into the sampling strategy are:

- Convenience Bias: Often found when time or money is serious constraint and sampling only done when it is convenient.
- Non response Bias: When a response is needed according to the sampling strategy but the response is incomplete or not provided at all.
- Response Bias: Occurs when response is misleading, inaccurate or untruthful. occurs when the responder feels pressure that occurs during data collection and influences the response (i.e. social pressure to be accepted and avoid conflict).
- Voluntary Response Bias: when sample members are self-selected volunteers or when the responders (often people) can choose whether to participate. The resulting sample tends to overrepresent individuals who have strong opinions.

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