Locally-Weighted-Regression Scatter-Plot Smoothing (LOWESS): A Graphical Exploratory Data Analysis Technique

Abstract

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 Robust Locally Weighted Regression Scatterplot Smoothing (LOWESS), a procedure that differs from other techniques because it smooths all of the points and works on 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. Additional keywords: Curve smoothing; Curve fitting; and APL programming language.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1984
Accession Number
ADA152239

Entities

People

  • Gary W. Moran

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Computer Programming
  • Computer Programs
  • Computers
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Information Science
  • Operations Research
  • Regression Analysis
  • Schools
  • Statistical Analysis
  • Test And Evaluation
  • Test Sets
  • United States
  • United States Naval Academy

Readers

  • Approximation Theory.
  • Software Engineering.

Technology Areas

  • Space
  • Space - Space Objects