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British Journal of Mathematical and Statistical Psychology, 1976
Regression component decompositions (RCD) are defined as a special class of component decompositions where the pattern contains the regression weights for predicting the observed variables from the latent variables. Compared to factor analysis, RCD has a broader range of applicability, greater ease and simplicity of computation, and a more logical and ...
Schönemann, Peter H., Steiger, James H.
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Regression component decompositions (RCD) are defined as a special class of component decompositions where the pattern contains the regression weights for predicting the observed variables from the latent variables. Compared to factor analysis, RCD has a broader range of applicability, greater ease and simplicity of computation, and a more logical and ...
Schönemann, Peter H., Steiger, James H.
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International Statistical Review / Revue Internationale de Statistique, 1992
Summary: The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. We pose alternative ways to view this model.
Olkin, Ingram, Yitzhaki, Shlomo
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Summary: The method of least squares ranks as one of the most commonly used methods for estimating the relation between a set of variables on the conditional expected value of another variable. Ordinary Least Squares (OLS) relies on several assumptions, which when violated may not yield robust estimates. We pose alternative ways to view this model.
Olkin, Ingram, Yitzhaki, Shlomo
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Statistical Papers, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kosfeld, Reinhold +1 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kosfeld, Reinhold +1 more
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Regression and Factor Analysis
Biometrika, 1973SUMMARY A basic model of factor analysis is employed in the estimation of multiple correlation coefficients and partial regression weights. Estimators are derived for situations in which some or all of the independent variates are subject to errors in measurement.
Lawley, D. N., Maxwell, A. E.
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Regression Analysis and Multivariate Analysis
Seminars in Reproductive Medicine, 1996Proper evaluation of data does not necessarily require the use of advanced statistical methods; however, such advanced tools offer the researcher the freedom to evaluate more complex hypotheses. This overview of regression analysis and multivariate statistics describes general concepts. Basic definitions and conventions are reviewed.
A J, Duleba, D L, Olive
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2018
Abstract This chapter teaches how to use R to conduct regression analysis to answer the question: Does trade promote economic growth? It demonstrates how to specify a statistical model from a theoretical argument, prepare data, estimate and interpret the statistical model, and use the estimated results to make inferences and answer the ...
Peter Filzmoser +2 more
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Abstract This chapter teaches how to use R to conduct regression analysis to answer the question: Does trade promote economic growth? It demonstrates how to specify a statistical model from a theoretical argument, prepare data, estimate and interpret the statistical model, and use the estimated results to make inferences and answer the ...
Peter Filzmoser +2 more
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Regression-Discontinuity Analysis
2008The regression discontinuity (RD) data design is a quasi-experimental evaluation design first introduced by Thistlethwaite and Campbell (1960) as an alternative approach to evaluating social programmes. The design is characterized by a treatment assignment or selection rule which involves the use of a known cut-off point with respect to a continuous ...
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Regression analysis is a widely used statistical technique that is used in a variety of fields, including the social sciences, engineering, finance, health care, and more. Regression is a tool that helps you understand the relationship between a dependent variable and one or more independent variables.
Bilal M. Ayyub, Richard H. McCuen
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Bilal M. Ayyub, Richard H. McCuen
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