BasisFunctions Goods Growth -Introduction -Figure 1 -Figure 2 -Conclusion Weather
 The Features of Human Growth Data Previous - 1 - 2 - 3 - 4 - Next: Figure 1 Expertise: Beginner Introduction The careful documentation of human growth is essential in order to define what we call normal growth, so that we can detect as early as possible when something is going wrong with the growth process. Auxologists, the scientists that specialize in the study of growth, also need high quality data to advance our understanding of how the body regulates its own growth. It may come as a surprise to learn that human growth at the macro level that we see in our children is not that well understood. Growth data over the entire growing period are exceedingly expensive to collect since children must be brought into the laboratory at preassigned ages over about a 20-year span. Meeting this observational regime requires great dedication and persistence by the parents, and the dropout rate is understandably high, even taking for granted the long-term commitment of maintaining a growth laboratory. The Fels Institute in Ohio, for example, has been collecting growth data since 1929, and is now measuring the third generation for some of its original cases. The accurate measurement of height is also difficult, and requires considerable training. Height diminishes throughout the day as the spine compresses, but it also depends on other factors. Infants must be measured lying down, and when the transition is made to measuring their standing height, measurements shrink by around one centimeter. The most careful procedures still exhibit standard deviations over repeated measurements of about three millimeters. Records of a child's height over 20 years display features, described below, that are difficult for a data analyst to model. The classic approach has been to use mathematical functions depending on a limited number of unknown constants, and auxologists have shown much ingenuity in developing these parametric models to capture these features. The best models have eight or more parameters, and are still viewed as possibly missing some aspects of actual growth. Nonparametric modeling techniques developed over the last three decades, such as kernel and spline smoothing methods, have been applied to growth data. These methods have been successful at detecting new features missed by parametric models, but they are not guaranteed to produce smoothing curves that are monotonic, or strictly increasing. Even a small failure of monotonicity in a height curve can have serious consequences for the corresponding growth velocity, and even more so for acceleration curves, which are especially important in identifying processes regulating growth. Previous - 1 - 2 - 3 - 4 - Next: Figure 1