In this Techniques article Peter Cahusac explains multiple regression, a much used statistical procedure, but one that is frequently misunderstood or misused. Multiple regression allows the effects of many explanatory (independent) variables on the measured (dependent) variable to be analysed simultaneously for situations when a single explanatory variable fails to account for most of the variation in the dependent variable – a common occurrence.