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Glossary

Whether you’re new to the quality and continuous improvement arena or a seasoned expert seeking detailed information on a specific quality topic, you should find what you need in our Glossary of Terms.

Explore the concepts, tools, applications and technical terms that make up the world of continuous improvement.

t-test

t-test is a hypothesis test of population means when small samples are involved.

For more information about t-test, click here.

Taguchi Loss Function

Taguchi Loss Function, a loss function that approximates the true long term loss due to deviations from target.

For more information about Taguchi Loss Function, click here.

Takt Time

Takt Time is a formula that defines the rate at which the customer buys the product or service and is calculated as: Takt Time = Total Available Time or Operating Time / The Customer Demand or Requirement.

For more information about Takt Time, click here.

Time Value Map

A Time Value Map is a pictorial description of value-added and non-value added time in a process.

For more information about Time Value Map, click here.

Tolerance

Tolerance is the permissible range of variation in a particular dimension of a product. Tolerance are often set by engineering requirements to ensure that components will function together properly.

For more information about Tolerance, click here.

Total Quality Management (TQM)

Total Quality Management is a management philosophy of integrated controls, including engineering, purchasing, financial administration, marketing and manufacturing, to ensure customer satisfaction and economical cost of quality.

For more information about Total Quality Management (TQM), click here.

Trend

A Trend is a gradual and systematic change with time or some other variable.

For more information about Trend, click here.

Type I Error

Type I Error is concluding the alternate hypothesis (H1) when the null hypothesis (H0) is really true.

For more information about Type I Error, click here.

Type II Error

Type II Error is concluding the null hypothesis (H0) when the alternate hypothesis (H1) is really true.

For more information about Type II Error, click here.

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