Analysis of variations in normal gait has received considerable attention over the last years. However, most such analyses are carried out on one explanatory variable at a time, and adjustments for other possibly influencing factors are often done using ad hoc methods. As a result, it can be difficult to know whether observed effects are actually a result of the variable under study. We wanted to simultaneously statistically test the effect of gender, age and walking speed on gait in a normal population, while also properly adjusting for the possibly confounding effects of body height and weight. Since point-by-point analysis does not take into account the time dependency in the data, we turned to functional data analysis (FDA). In FDA the whole gait curve is represented not by a set of points, but by a mathematical function spanning the whole gait cycle. We performed several multiple functional regression analyses, and the results indicate that walking speed is the main factor influencing gait in the reference material at our motion analysis laboratory. This effect is also largely unaffected by the presence of other variables in the model. A gender effect was also apparent in several planes and joints, but this effect was often more outspoken in the multiple than in the univariate regression analyses, highlighting the importance of adjusting for confounders like body height and weight.