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Journal ArticleDOI

Tables for An Approximate Test for Outliers in Linear Models

Richard E. Lund
- 01 Nov 1975 - 
- Vol. 17, Iss: 4, pp 473-476
TLDR
In this article, a test for the largest residual being an outlier is implemented through table development, and tables of critical valltes for tests at levels (α ≤ 0.10, 00.5, and 0.01) are included.
Abstract
Residuals from fit are often examined in regression analysis. A test suggested by Ellenberg [5] and Prescott [7] for the largest residual being an outlier is implemented through table development. Tables of critical valltes for tests at levels (α ≤ 0.10, 00.5, and 0.01 are included.

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Citations
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Journal ArticleDOI

Detection of influential observation in linear regression

TL;DR: In this article, a measure based on confidence ellipsoids is developed for judging the contribution of each data point to the determination of the least squares estimate of the parameter vector in full rank linear regression models.
Book

Design and Analysis of Cross-Over Trials

TL;DR: In this article, the authors provide an overview of recent developments in the design and analysis of cross-over trials and present methods for testing for a treatment difference when the data are binary.
Journal ArticleDOI

Influential Observations in Linear Regression

TL;DR: In this article, the authors investigated the characteristics of observations which cause them to be influential in least square analysis and related to residual variances, residual correlations, and the convex hull of the observed values of the independent variables.
Journal ArticleDOI

New improved equations for , and SiO2 geothermometers by outlier detection and rejection

TL;DR: In this paper, the authors presented new improved equations for three still widely used NaK, NaLi and SiO2 geothermometers (obtained by statistical treatment of the data and application of outlier detection and rejection as well as theory of error propagation).
References
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Book

Applied Regression Analysis

TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.
Journal ArticleDOI

Procedures for Detecting Outlying Observations in Samples

TL;DR: In this paper, a procedure for determining statistically whether the highest observation, lowest observation, highest and lowest observations, or more of the observations in the sample are statistical outliers is given.
Journal ArticleDOI

The Examination and Analysis of Residuals

TL;DR: A number of methods for examining the residuals remaining after a conventional analysis of variance or least-squares fitting have been explored during the past few years as discussed by the authors, and a variety of these techniques more easily available, so that they can be tried out more widely.
Journal ArticleDOI

Rejecting Outliers in Factorial Designs

TL;DR: In this paper, a general procedure for calculating critical values of the maximum normed residual is given. But the procedure is not suitable for a two-way layout with one observation per cell, which can be used as a replacement of Table 2.
Journal ArticleDOI

An Approximate Test for Outliers in Linear Models

TL;DR: In this article, a test statistic for detecting outliers in linear models involving residuals standardized by their individual standard deviations is considered and it is suggested that its critical values are adequately approximated by upper bounds for the critical values of a similar test statistic involving residual values standardized by a constant standard deviation.