Multiple Gross and Systematic Errors Detected in Surveying Measurements Using Statistical Quality Control

Abstract

Much of the surveyor’s task involves the acquisition and analysis of survey measurements. Such measurements are subject to random, systematic and gross errors. In practice, redundant measurements are made to provide quality control and check for errors. In qualitative analysis and statistical evaluation, it is generally assumed that the measurements contain only random errors and are regarded as random variables. In reality, the measurements may contain gross and/or systematic errors. The effects of such errors are distributed over the residuals, after an adjustment and lead to questionable results and interpretation. For high precision applications, gross and systematic errors need to be detected prior to the analysis. These errors should be tackled before an adjustment and evaluation by means of screening. These few remaining gross and systematic errors in the measurements can be detected after an adjustment. These adjustment methods assume the presence of only one gross or systematic error. One of the most effective methods that can be used in detecting multiple gross and systematic errors is the method of statistical quality control. Statistical quality control is a technique to monitor a procedure with a goal of making it more efficient and ensures precise results. Statistical control charts are used to provide an operational definition of a special cause for a given set of data. It is possible to construct multiples of sigma control limits. When all the points on a control chart are within a multiple of sigma control limits and there are no gross or systematic patterns in the data, the process of measurements is said to be in a I state of statistical control. Otherwise, the data indicate the presence of non-random gross or systematic errors. Different methods of statistical quality control were used in this research. The main conclusion reached out, using statistical quality control, is that this method can be used successfully in detecting multiple gross and systematic errors.