Quality control and improvement based on grouped data

Abstract

<p>This thesis develops quality control and improvement techniques based on grouped data. Grouped data occur frequently in industry. However, in the past, most techniques have failed to directly take this grouping into account, and as a result do not perform well in many circumstances. Two major areas of application are considered. First, acceptance sampling plans, acceptance control charts, and Shewhart control charts based on grouped data are developed. These forms of statistical process control have broad application and are in use widely. The design and implementation methodology is derived assuming either a normal or Weibull process, but is easily adapted to any other underlying distribution. A number of design approaches are presented and their relative advantages and disadvantages are discussed. The second application involves estimating the correlation between destructively measured strength properties. This problem arises in the area of structural design. To obtain an estimate of the correlation censoring of the strength data is required. The censoring or proof-testing results in grouped data. A number of simple estimation procedures are presented and compared.</p>