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This lecture covers the behavioral motivation behind nonlinear specifications in data preprocessing, using examples with travel time to illustrate how the perception of time varies for different trip durations. It also discusses assumptions about the impact of travel time on utility and how data can be preprocessed to account for nonlinearities in variables, highlighting the importance of interactions between attributes and socio-economic characteristics.