This book grew out of class notes for a course that served two purposes:

1. To familiarize graduate students in hydrogeology and environmental engineering with some practical methods for solving interpolation and related estimation problems. The course emphasized geostatistical methods.

2. To illustrate how one may use data to develop empirical models, that is, to provide an introduction to applied statistical modeling.

Engineers and applied geophysicists routinely encounter estimation problems: From data, they must surmise the values of unknown variables. A case in point is drawing the map of the free surface (elevation of water table) in an aquifer from observations of the free-surface elevation at a few wells in addition to other information. A practitioner faces such estimation problems much more often than some other problems that are covered in the academic curriculum. And yet most practitioners have received no formal instruction on estimation and, in many cases, are unable to judge the applicability of a method or to interpret the results produced by software they use. Their efforts to develop a grasp of the subject are frustrated by unfamiliar jargon. Indeed, the type of training one receives in mathematics and physics does not help one to develop the skills needed for using data to build empirical models. I believe that it is absurd to expect one to “pick up” estimation methods without some systematic training and a fair degree of effort.

After teaching the course for about ten years, I realized that there might be room for a textbook that presumes no background in statistics and that uses common sense to motivate and justify methods used for estimation of spatial functions.