<p>This research makes a theoretical contribution by providing straightforward and coherent derivation of a logistic model, and then estimating the parameters of the model with a fishing data set. The logistic model is frequently considered as a convenient regression model to find the associations between a binary outcome variable and several covariates. This is also a model that has numerous practical applications, as in banking, engineering, social sciences, medical research and biostatistics. In the paper, we briefly summarize the function and estimating equation of the logistic model. We next investigate the large sample properties of this model under some regularity conditions. We then provide a simulation study of the work. A factual application of the logistic model is illustrated using a fishing data set. The results have consilience with practice. It also shows that this is a reliable model to maximize the number of fish while fishing. Finally, some applications in decision sciences, some concluding remarks, and future research directions are discussed.</p>

doi.org/10.47654/v25y2021i2p74-104, hdl.handle.net/1765/136025
Advances in Decision Sciences
Erasmus School of Economics