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# ? LSA ? - Least Square Adjustment and Best coordinates estimated of observations

I was wondering if it is possible to run JASP to do a network adjustment by LS or any regression algorithm,

Taking a matrix / list of coordinates of points (cartesian XYZ) , I would like to run a BEST-FIT model and test it statiscally to be sure my solution is the best.

However, relying only on the results of a single set of coordinates is risky, since there is no way to tell whether the position defining those coordinates are correct. Coordinates computed from measurements from other existing points can be compared with the coordinates computed by the first set of measurements. Generally, the more measurements defining a single point, the more reliable its coordinates are and the more confidence there is in detecting mistake measurements. These additional measurements are called redundant measurements.

All measurements contain some degree of error. Redundant measurements will compute slightly different coordinates for the same point. Since there can only be one coordinate location for a point, best-estimate coordinates for the point can be derived by computing a weighted average of the redundant measurements, with each weight defined by the measurement accuracy. The higher the accuracy of the measurement, the higher its weight and the more influence it will have in computing the best-estimate coordinates of the point.

We ca think of it as a network, linking point A-C-H-B-D etc , setting a confidence interval and a parameter for max error, like 0.0001 (sigma), testing the estimated modelled values statistically and then providing a result of best-fit, with the corrections made/accepted ov er the original coordinates X,Y,Z - finding the line that minimizes the square of the errors between the line and the data points.

Any idea how to run this on JASP?

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