Titlebook: Understanding Regression Analysis; Michael Patrick Allen Book 1997 Springer Science+Business Media New York 1997 Nation.Regression analysi

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Separating explained and error variance,wever, we create two new variables. One of these variables, ŷ, is the predicted value of the dependent variable using the linear regression equation and the independent variable. The other variable, e, is the error of prediction obtained using this regression equation. This error value is sometimes

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Regression analysis with standardized variables,regression coefficient between two standardized variables is equal to the covariance of the standardized variables. This result can be seen from the following equation for the regression coefficient: .In order to avoid confusion, the standardized regression coefficient, b*., is denoted with an aster

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Populations, samples, and sampling distributions, we are not usually interested in the characteristics of a particular sample. More often, we are interested in estimating the characteristics of the population from which the sample was drawn. Whenever we wish to make statements about the characteristics of a population, based on the characteristics

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More matrix algebra: Manipulating matrices,y to employ matrix algebra. Indeed, a basic familiarity with matrix algebra is also essential to understanding most of the techniques of multivariate statistical analysis. Matrices are simply rectangular arrays of elements. Consider, for example, the matrix G as follows: .This matrix is said to be “

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The multiple regression model,seful, or powerful, as multiple regression analysis. .. As such, multiple regression is merely an extension of simple regression. Indeed, the logic of multiple regression analysis is essentially identical to that of simple regression analysis. In simple regression analysis, we predict the observed v