Understanding regression analysis /
Allen, Michael Patrick.
Understanding regression analysis / Michael Patrick Allen. - New York : Plenum Press, �1997. - 1 online resource (ix, 216 pages) : illustrations
Includes bibliographical references (pages 210-212) and index.
Origins and uses of regression analysis -- Basic matrix algebra: manipulating vectors -- Mean and variance of a variable -- Regression models and linear functions -- Errors of prediction and least-squares estimation -- Covariance and linear independence -- Separating explained and error variance -- Transforming variables to standard form -- Regression analysis with standardized variables -- Populations, samples, and sampling distributions -- Sampling distributions and test statistics -- Testing hypotheses using the t test -- t test for the simple regression coefficient -- More matrix algebra: manipulating matrices -- Multiple regression model -- Normal equations and partial regression coefficients -- Partial regression and residualized variables -- Coefficient of determination in multiple regression -- Standard errors of partial regression coefficents -- Incremental contributions of variables -- Testing simple hypotheses using the f test -- Testing for interaction in multiple regression -- Nonlinear relationships and variable transformations -- Regression analysis with dummy variables -- One-way analysis of variance using the regression model -- Two-way analysis of variance using the regression model -- Testing for interaction in analysis of variance -- Analysis of covariance using the regression model -- Interpreting interaction in analysis of covariance -- Structural equation models and path analysis -- Computing direct and total effects of variables -- Model specification in regression analysis -- Influential cases in regression analysis -- Problem of multicollinearity -- Assumptions of ordinary least-squares estimation -- Beyond ordinary regression analysis.
Proceeding on the assumption that it is possible to develop a sufficient understanding of this technique without resorting to mathematical proofs and statistical theory, Understanding Regression Analysis explores Descriptive statistics using vector notation and the components of a simple regression model; the logic of sampling distributions and simple hypothesis testing; the basic operations of matrix algebra and the properties of the multiple regression model; the testing of compound hypotheses and the application of the regression model to the analysis of variance and covariance; and structural equation models and influence statistics. This user-friendly text encourages an intuitive grasp of regression analysis by deferring issues of statistical inference until the reader has gained some experience with the purely descriptive properties of the regression model. It is an excellent, practical guide for advanced undergraduate and postgraduate students in social science courses covering sociology, political science, anthropology, and psychology, and a worthwhile primer for researchers and policy analysts with no formal training in statistics.
0585256578 9780585256573
Regression analysis.
Statistics.
Matrices.
Structural equation modeling.
MATHEMATICS--Probability & Statistics--Regression Analysis.
Matrices.
Regression analysis.
Statistics.
Structural equation modeling.
Electronic books.
QA278.2 / .A434 1997eb
519.536
Understanding regression analysis / Michael Patrick Allen. - New York : Plenum Press, �1997. - 1 online resource (ix, 216 pages) : illustrations
Includes bibliographical references (pages 210-212) and index.
Origins and uses of regression analysis -- Basic matrix algebra: manipulating vectors -- Mean and variance of a variable -- Regression models and linear functions -- Errors of prediction and least-squares estimation -- Covariance and linear independence -- Separating explained and error variance -- Transforming variables to standard form -- Regression analysis with standardized variables -- Populations, samples, and sampling distributions -- Sampling distributions and test statistics -- Testing hypotheses using the t test -- t test for the simple regression coefficient -- More matrix algebra: manipulating matrices -- Multiple regression model -- Normal equations and partial regression coefficients -- Partial regression and residualized variables -- Coefficient of determination in multiple regression -- Standard errors of partial regression coefficents -- Incremental contributions of variables -- Testing simple hypotheses using the f test -- Testing for interaction in multiple regression -- Nonlinear relationships and variable transformations -- Regression analysis with dummy variables -- One-way analysis of variance using the regression model -- Two-way analysis of variance using the regression model -- Testing for interaction in analysis of variance -- Analysis of covariance using the regression model -- Interpreting interaction in analysis of covariance -- Structural equation models and path analysis -- Computing direct and total effects of variables -- Model specification in regression analysis -- Influential cases in regression analysis -- Problem of multicollinearity -- Assumptions of ordinary least-squares estimation -- Beyond ordinary regression analysis.
Proceeding on the assumption that it is possible to develop a sufficient understanding of this technique without resorting to mathematical proofs and statistical theory, Understanding Regression Analysis explores Descriptive statistics using vector notation and the components of a simple regression model; the logic of sampling distributions and simple hypothesis testing; the basic operations of matrix algebra and the properties of the multiple regression model; the testing of compound hypotheses and the application of the regression model to the analysis of variance and covariance; and structural equation models and influence statistics. This user-friendly text encourages an intuitive grasp of regression analysis by deferring issues of statistical inference until the reader has gained some experience with the purely descriptive properties of the regression model. It is an excellent, practical guide for advanced undergraduate and postgraduate students in social science courses covering sociology, political science, anthropology, and psychology, and a worthwhile primer for researchers and policy analysts with no formal training in statistics.
0585256578 9780585256573
Regression analysis.
Statistics.
Matrices.
Structural equation modeling.
MATHEMATICS--Probability & Statistics--Regression Analysis.
Matrices.
Regression analysis.
Statistics.
Structural equation modeling.
Electronic books.
QA278.2 / .A434 1997eb
519.536
