Capability study

Scope: continuous quality metric (Y) following a normal distribution. For example: duration, temperature, size, etc.

Here, we determine the expected (probabilistic) defect rate from what is observed in a sample. The calculation also provides the margin of error (statistical precision) on the expected defect rate.

Enter the following data :

• n: sample size
• Xbar: sample mean
• s: sample standard deviation
• LSL: Lower Specification Limit
• USL: upper specification limit

Scope: continuous quality metric (Y) following a normal distribution. For example: duration, temperature, size, etc.

Here we determine the minimum sample size to be taken to know the expected defect rate with a certain acceptable margin of error (statistical precision).

Enter the following data:

• Relative margin of error: requirement for an acceptable percentage of error in the knowledge of the defective rate
• Assumed performance: an estimate of the order of magnitude of operational performance, expressed as a defect rate or as Sigma.

Scope: Quality metric (Y) for counting according to a Poisson distribution. For example: number of defects per PC, number of hold times per phone call, number of errors per document, etc.

Here we determine the expected (probabilistic) defect rate from what is observed in a sample. The calculation also provides the margin of error (statistical precision) on the expected defect rate. (Normal distribution approximation method)

Enter the following data :

• n: sample size
• λ̂: rate of occurrences in the sample; typically the rate of defects per unit (DPU).

Scope: Quality metric (Y) for counting according to a Poisson distribution. For example: number of defects per PC, number of hold times per phone call, number of errors per document, etc.

Here we determine the minimum sample size to be taken to know the expected defect rate with a certain acceptable margin of error (statistical precision).(approximation method according to the normal distribution)

Enter the following data:

• Relative margin of error: requirement for an acceptable percentage of error in the knowledge of the defective rate
• Assumed performance: an estimate of the order of magnitude of operational performance, expressed as a defect rate or as Sigma.

Scope: Binary quality metric (Y) following a binomial distribution. Examples: abandoned calls, incorrect invoices, defective parts, etc.

Here we determine the expected (probabilistic) defect rate from what is observed in a sample. The calculation also provides the margin of error (statistical precision) on the expected defect rate. (Normal distribution approximation method)

Enter the following data:

• n: sample size
• p: proportion of defective units in the sample

If no defective units were observed in the sample, introduce a p-value equal to 1/(n+2).