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This example demonstrates how one might develop a model for estimating construction costs, as well as performance gains resulting from What'sBest!'s exclusive linearization option. As you will see, the model, as formulated, is nonlinear. However, after invoking the linearization option in What'sBest!, the solver automatically converts this nonlinear model to an equivalent linear model.

Objective of Optimization:
You will use your historical data to estimate values for the beta coefficients (â1, â2, â3,, and â4) in formula (i). You want your estimation function to avoid the case where it might be a poor predictor on one type of home. Thus, you have decided you want your model’s objective to minimize the maximum error in your cost estimations. Note that this process of fitting a linear function to a set of data points is referred to as linear regression.