Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS)
Subtitle: a graphical exploratory data analysis technique
By: Gary W. Moran
Publisher: Naval Postgraduate School
Published: 1984
Place of publication: Monterey, Calif
Format: [electronic resource] :
Subjects: Regression analysis; Curve fitting; Vision; Databases
Description: 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.
Notes: Thesis Advisor(s): Lewis, Peter A.W. ; Second Reader: Gaver, D.P.

September 1984.

Author(s) subject terms: curve smoothing, regression, curve fitting

Description based on title screen as viewed on August 3, 2011.

DTIC Descriptor(s): Regression Analysis, Data Bases, Programming Languages, Theses, Weighting Functions, Fortran, Least Squares Method, Vision, Curve Fitting

DTIC Identifier(s): Smoothing(Mathematics), LOWESS(Locally Weighted Regression Scatter Plot Smoothing), APL programming language

US Navy (USN) author.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, 1984.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 1984.

Includes bibliographical references (p. 83-84).

Approved for public release, distribution unlimited--Cover.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Record upgraded 8/3/11 daw