Problems with multiple interdependent components offer a better representation of the real-world situations where globally optimal solutions are preferred over optimal solutions for the individual components. One such model is the Travelling Thief Problem (TTP); while is appears popular and while it may offer a better benchmarking alternative to the standard benchmarks, only one form of inter-component dependency is investigated. The goal of this paper is to study the impact of different models of dependency on the fitness landscape using performance prediction models (regression analysis). To conduct the analysis, we consider a generalised model of the TTP, where the dependencies between the two components of the problem are tunable via the problem features. The regression model was able to predict the expected runtime of the algorithm based on the problem features. Furthermore, the results show that the contribution of the item value drop dependency is significantly higher than the velocity change dependency.