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Factor analysis regression [PDF]
Statistical Papers, 2006 In the presence of multicollinearity the literature points to principal component regression (PCR) as an estimation method for the regression coefficients of a multiple regression model. Due to ambiguities in the interpretation, involved by the orthogonal transformation of the set of explanatory variables, the method could not yet gain wide acceptance.
Kosfeld, Reinhold +1 more
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2019
Linear regression models a dependent variable Y in terms of a linear combination of p independent variables X=[X1|...|Xp] and estimates the coefficients of the combination using independent observations (x_i,Y_i ),i=1,...,n. The Gauss-Markov conditions guarantees that the least squares estimate of the regression coefficients constitutes the best linear
ANGELINI, Claudia
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Linear regression models a dependent variable Y in terms of a linear combination of p independent variables X=[X1|...|Xp] and estimates the coefficients of the combination using independent observations (x_i,Y_i ),i=1,...,n. The Gauss-Markov conditions guarantees that the least squares estimate of the regression coefficients constitutes the best linear
ANGELINI, Claudia
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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.
A. E. Maxwell, D. N. Lawley
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1998
So far we have considered only one regressor X besides the constant in the regression equation. Economic relationships usually include more than one regressor. For example, a demand equation for a product will usually include real price of that product in addition to real income as well as real price of a competitive product and the advertising ...
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So far we have considered only one regressor X besides the constant in the regression equation. Economic relationships usually include more than one regressor. For example, a demand equation for a product will usually include real price of that product in addition to real income as well as real price of a competitive product and the advertising ...
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Logistic Regression Analysis [PDF]
, 1990 In chapter 8 the connection to log-linear models for contingency tables was stressed. The direct connection to regression analysis for continuous response variables will now be brought more clearly into focus. Assume as before that the response variable is binary and that it is observed together with p explanatory variables.
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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 ...
James H. Steiger, Peter H. Schönemann
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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 ...
James H. Steiger, Peter H. Schönemann
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Regression analysis and dependence
Metrika, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
José M. González-Barrios +1 more
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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.
David L. Olive, Antoni J. Duleba
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