Statistical regression applied to borehole strain measurements data analysis

In mining and geotechnical engineering, it is usually necessary to carry out field measurements in order to obtain information. Parameters are often measured indirectly and calculated based on certain relationships to the measured quantities. More often, the number of measurements taken is greater than the minimum required, in order to increase the reliability of results. However, some data points are less reliable than others for reasons such as measurement errors; a solution which best fits the measurement data is obtained accordingly. As a result, there is a residual or a difference between the individual quantities measured and those predicted from the best-fit solution. This brings about a question of how big a residual is acceptable for a solution to be reliable. It is also important to know whether the data point with the largest residual is the most erroneous, whether those data points with large residuals should be deleted and how many of them should be deleted. Standard deviation may provide a measure of the data divergence but it is questionable if this parameter can be used as a measure of the reliability of solution. In order to solve these problems, the author has done extensive study in this area, especially as part of geotechnical data analysis. In this paper, the statistical multiple regression method is introduced to analyse the measurement data. The method is applied to the analysis of in situ stress measurement and can be easily adopted to analyse data from other field measurements and laboratory tests. An example is included which illustrates the analysis procedure and shows the advantages of the method.