<div class="description">
<dl><dd>Thesis (M.S. in Operations Research)--Naval Postgraduate School, 1984</dd>
<dd>Thesis Advisor(s): Lewis, Peter A.W. ; Second Reader: Gaver, D.P</dd>
<dd>September 1984</dd>
<dd>Author(s) subject terms: curve smoothing, regression, curve fitting</dd>
<dd>Description based on title screen as viewed on August 3, 2011</dd>
<dd>DTIC Descriptor(s): Regression Analysis, Data Bases, Programming Languages, Theses, Weighting Functions, Fortran, Least Squares Method, Vision, Curve Fitting</dd>
<dd>DTIC Identifier(s): Smoothing(Mathematics), LOWESS(Locally Weighted Regression Scatter Plot Smoothing), APL programming language</dd>
<dd>US Navy (USN) author</dd>
<dd>Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 1984</dd>
<dd>Includes bibliographical references (p. 83-84)</dd>
<dd>Statisticians have long used moving average type smoothing and classical regression analysis techniques to reduce the variability in data sets and enhance the visual information presented by scatterplots. This thesis examines the effectiveness of Robuts Locally Weighted Regression Scatterplot Smoothing (LOWESS), a procedure that differs from other techniques because it smooths all of the points and works unequally as well as equally spaced data. The LOWESS procedure is evaluated by comparing it to previously validated uniform and cosine weighted moving average and least squares regression programs. Interactive APL and FORTRAN programs and detailed user instructions are included for use by interested readers</dd>
<dd>Mode of access: World Wide Web</dd>
<dd>System requirements: Adobe Acrobat Reader</dd>
<dd>Record upgraded 8/3/11 daw</dd></dl><br>Subjects: Curve fitting; Regression analysis; Databases; Vision</div>
<div class="fn value">
Moran, Gary W.</div>
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