This work is about fracture conductivity; how to measure and model it based on experimental data. It is also about how to determine the relative importance of the factors that affect its magnitude and how to predict its magnitude based on these factors. We dynamically placed the slurry hereby simulating the slurry placement procedure in a field-scale fracture. We also used factorial and fractional factorial designs as the basis of our experimental investigation. The analysis and interpretation of experimental results take into account the stochastic nature of the process. We found that the relative importance of the investigated factors is dependent on the presence of outliers and how they are handled. Based on our investigation we concluded that the investigated factors arranged in order of decreasing impact on conductivity are: closure stress, polymer loading, flow back rate, presence of breaker, temperature and proppant concentration. In particular, we find that at high temperatures, fracture conductivity was severely reduced due to the formation of a dense proppant-polymer cake. Also, dehydration of the residual gel in the fracture at high flow back rates appears to cause severe damage to conductivity at higher temperatures. This represents a new way of thinking about the fracture cleanup process; not only as a displacement process, but also as a displacement and evaporative process. In engineering practice, this implies that aggressive flow back schemes are not necessarily beneficial for conductivity development. Also, we find that at low proppant concentrations, there is the increased likelihood of the formation of channels and high porosity fractures resulting in high fracture conductivities. The uniqueness of this work is a focus on the development of a conductivity model using regression analysis and also the illustration of a procedure that can be used to develop a conductivity model using dimensional analysis. We reviewed both methodologies and applied them to the challenge of modeling fracture conductivity from experimental studies.