3 credits
Fall 2025 Lecture Upper Division
This course presents a thorough and comprehensive look at the topic of fitting data to a mathematical model. The techniques presented will free the scientist or engineer from dependence on restrictive software applications, and allow customization of solutions using weighting, constraints, parameter dependencies, and robust techniques which minimize the influence of blunders. Example applications include 2D/3D ranging, 2D/3D triangulation, curve and surface fitting, coordinate transformations, leveling, and image triangulation. Pre-analysis and design techniques permit the precision of unknown parameters to be determined in advance, prior to expending time and effort in field measurements.
Learning Outcomes1Perform error/covariance propagation to make quantitative statements about uncertainties of the results, and to evaluate statistical hypothesis tests.
2Become conversant with both linear and nonlinear models as well as sequential estimation.
Restrictions
made by Purdue students.