Lichti, Derek DChan, Ting OnBelton, David2021-02-082021-02-082021-01-18Lichti, D. D., Chan, T. O., & Belton, D. (2021). Linear regression with an observation distribution model. Journal of Geodesy, 95(2). doi:10.1007/s00190-021-01484-xhttp://hdl.handle.net/1880/113083This is a post-peer-review, pre-copyedit version of an article published in Journal of Geodesy. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00190-021-01484-x”Despite the high complexity of the real world, linear regression still plays an important role in estimating parameters to model a physical relationship between at least two variables. The precision of the estimated parameters, which can usually be considered as an indicator of the solution quality, is conventionally obtained from the inverse of the normal equations matrix for which intensive computation is required when the number of observations is large. In addition, the impacts of the distribution of the observations on parameter precision are rarely reported in the literature. In this paper, we propose a new methodology to model the distribution of observations for linear regression in order to predict the parameter precision prior to actual data collection and performing the regression. The precision analysis can be readily performed given a hypothesized data distribution. The methodology has been verified with several simulated and real datasets. The results show that the empirical and model-predicted precisions match very well, with discrepancies of up to 6% and 3.4% for simulated and real datasets, respectively. Simulations demonstrate that these differences are simply due to finite sample size. In addition, simulation also demonstrates the relative insensitivity of the method to noise in the independent regression variables that causes deviations from the data distribution function. The proposed methodology allows straightforward prediction of the parameter precision based on the distribution of the observations related to their numerical limits and geometry, which greatly simplify design procedures for various experimental setups commonly involved in geodetic surveying such as LiDAR data collection.engUnless otherwise indicated, this material is protected by copyright and has been made available with authorization from the copyright owner. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.regressionleast-squaresestimationobservation distributionnormal equationsjournal articleRGPIN-2018-0377510.1007/s00190-021-01484-x