Recently, measurement based studies of software systems prolifirated, reflecting an increasingly empirical focus on system availability, reliability, aging and fault tolerance. However, it is a non-trivial, error-prone, arduous, and time-consuming task even for experienced system administrators and statistical analysts to know what a reasonable set of steps should include to model and successfully predict performance variables or system failures of a complex software system. Reported results are fragmented and focus on applying statistical regression techniques to captured numerical system data. In this paper, we propose a best practice guide for building empirical models based on our experience with forecasting Apache web sewer performance variables and forecasting call availability of a real world telecommunication system. To substantiate the presented guide and to demonstrate our approach step-by-step we model and predict the response time and the amount of free physical memory of an Apache web sewer system. Additionally, we present concrete results for a) variable selection where we cross benchmark three procedures, b) empirical model building where we cross benchmark four techniques and c) sensitivity analysis. This besr practice guide intends to assist in configuring modeling approaches systematically for best estimation andprediction results.