Smoothing can assist data analysis by highlighting important trends and revealing long term movements in time series that otherwise can be hard to see.
Many data smoothing techniques have been developed, each of which may be useful for particular kinds of data and in specific applications. David will give an introductory overview of the most common smoothing methods, and will show examples of their use. He will cover moving averages, exponential smoothing, the Kalman Filter, low-pass filters, high pass filters, LOWESS and smoothing splines.
This presentation is pitched towards those who may use smoothing techniques during the course of their analytic work, but who have little familiarity with the techniques themselves. David will avoid the underpinning mathematical and statistical methods, but instead will focus on providing a clear understanding of what each technique is about.
About the Instructor
David Lillis is an applied statistician in Wellington, New Zealand.
His company, Sigma Statistics and Research Limited, provides online instruction, face-to-face workshops on R, and coding services in R.
David holds a doctorate in applied statistics and is a frequent contributor to The Analysis Factor, including our blog series R is Not So Hard.
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