Journal ArticleDOI
Building Multiple Regression Models Interactively
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This paper used interactive computing and exploratory methods to discover unexpected features of the data, such as nonlinearity, collinearity, outliers, and points with high leverage.Abstract:
Automated multiple regression model-building techniques often hide important aspects of data from the data analyst. Such features as nonlinearity, collinearity, outliers, and points with high leverage can profoundly affect automated analyses, yet remain undetected. An alternative technique uses interactive computing and exploratory methods to discover unexpected features of the data. One important advantage of this approach is that the data analyst can use knowledge of the subject matter in the resolution of difficulties. The methods are illustrated with reanalyses of the two data sets used by Hocking (1976, Biometrics 32, 1-44) to illustrate the use of automated regression methods.read more
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Applied Predictive Modeling
Max Kuhn,Kjell Johnson +1 more
TL;DR: This research presents a novel and scalable approach called “Smartfitting” that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of designing and implementing statistical models for regression models.
Journal ArticleDOI
A Baseline for the Multivariate Comparison of Resting-State Networks
Elena A. Allen,Erik B. Erhardt,Eswar Damaraju,William Gruner,William Gruner,Judith M. Segall,Judith M. Segall,Rogers F. Silva,Rogers F. Silva,Martin Havlicek,Martin Havlicek,Srinivas Rachakonda,Jill Fries,Ravi Kalyanam,Ravi Kalyanam,Andrew M. Michael,Arvind Caprihan,Jessica A. Turner,Jessica A. Turner,Tom Eichele,Steven Adelsheim,Angela D. Bryan,Angela D. Bryan,Juan R. Bustillo,Vincent P. Clark,Vincent P. Clark,Sarah W. Feldstein Ewing,Francesca M. Filbey,Francesca M. Filbey,Corey C. Ford,Kent E. Hutchison,Kent E. Hutchison,Rex E. Jung,Rex E. Jung,Kent A. Kiehl,Kent A. Kiehl,Piyadasa W. Kodituwakku,Yuko M. Komesu,Andrew R. Mayer,Andrew R. Mayer,Godfrey D. Pearlson,John P. Phillips,John P. Phillips,Joseph Sadek,Michael Stevens,Ursina Teuscher,Ursina Teuscher,Robert J. Thoma,Vince D. Calhoun +48 more
TL;DR: A multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing is introduced and is demonstrated by identifying the effects of age and gender on the resting-state networks of 603 healthy adolescents and adults.
Journal ArticleDOI
Multivariate analysis in ecology and systematics: panacea or pandora's box?
TL;DR: In a survey of the literature, this work found 20 major summaries of recent applications of multivariate analysis in ecology and systematics, with an increasing interest in multivariate methods.
Journal ArticleDOI
Influential Observations, High Leverage Points, and Outliers in Linear Regression
Samprit Chatterjee,Ali S. Hadi +1 more
TL;DR: In this article, a bewilderingly large number of statistical quantities have been proposed to study outliers and influence of individual observations in regression analysis and the inter-relationships which exist among the proposed measures.
MonographDOI
Statistical design and analysis of experiments
TL;DR: This paper describes the design and analysis of experiments and the results obtained showed clear patterns in the designs and the analysis of the experiments showed clear conclusions about the aims and objectives of the study.
References
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Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
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.