J.O. Ramsay's Applied functional data analysis : methods and case studies PDF

By J.O. Ramsay

Practical info research is a brand new region of statistical study, and the 2 authors of this name current the 1st selection of tools in book-form. a lot of the paintings is unique to the authors. entrance topic; creation; existence direction info in Criminology; The Nondurable items Index; Bone Shapes from a Paleopathology examine; Modeling Reaction-Time Distributions; Zooming in on Human development; Time Warping Handwriting and climate files; How Do Bone Shapes point out Arthritis?; sensible types for try goods; Predicting Lip Acceleration from Electromyography; The Dynamics of Handwriting revealed Characters; A Differential Equation for Juggling; again subject

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We observed, for example, that the curve was too smooth if it underestimated peak values such as that of June year after year, or if it consistently overestimated low values such as July. We also learned a lot by looking at the residuals from the fit, computed by subtracting the fitted from the actual value. If there was some trend running over several months, this was a sign that we had oversmoothed the data. At this stage, one may say, it is rather easier to detect oversmoothing than undersmoothing.

We still see fairly large seasonal oscillations, but they are now much smoother, and hence show less variation in velocity and acceleration. 10 showing the 1997 cycles on a larger scale, we see that there are now four cycles rather than three, and that the final winter cycle has a strongly negative net velocity. Are this loss of dynamism and these structural changes due to the fact that production is no longer so dependent on manpower? Or, perhaps, that it is more tightly controlled by information technology?

3. We actually plotted the data and the fit separately for each of the 51 years of interest, and noted where the curve seemed to miss the data repeatedly. We observed, for example, that the curve was too smooth if it underestimated peak values such as that of June year after year, or if it consistently overestimated low values such as July. We also learned a lot by looking at the residuals from the fit, computed by subtracting the fitted from the actual value. If there was some trend running over several months, this was a sign that we had oversmoothed the data.

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