Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.

Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.

Approved for public release; distribution is unlimited. === 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 effectiven...

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Main Author: Moran, Gary W.
Other Authors: Lewis, P.A.W.
Language:en_US
Published: Monterey, California. Naval Postgraduate School 2012
Online Access:http://hdl.handle.net/10945/19419
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spelling ndltd-nps.edu-oai-calhoun.nps.edu-10945-194192015-02-13T03:56:24Z Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique. Moran, Gary W. Lewis, P.A.W. Gaver, D.P. Naval Postgraduate School Naval Postgraduate School Operations Research Approved for public release; distribution is unlimited. 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 2012-11-19T23:50:35Z 2012-11-19T23:50:35Z 1984-09 Thesis http://hdl.handle.net/10945/19419 en_US This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States. Monterey, California. Naval Postgraduate School
collection NDLTD
language en_US
sources NDLTD
description Approved for public release; distribution is unlimited. === 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
author2 Lewis, P.A.W.
author_facet Lewis, P.A.W.
Moran, Gary W.
author Moran, Gary W.
spellingShingle Moran, Gary W.
Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.
author_sort Moran, Gary W.
title Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.
title_short Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.
title_full Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.
title_fullStr Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.
title_full_unstemmed Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS) : a graphical exploratory data analysis technique.
title_sort locally-weighted-regression scatter-plot smoothing (lowess) : a graphical exploratory data analysis technique.
publisher Monterey, California. Naval Postgraduate School
publishDate 2012
url http://hdl.handle.net/10945/19419
work_keys_str_mv AT morangaryw locallyweightedregressionscatterplotsmoothinglowessagraphicalexploratorydataanalysistechnique
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