“Estimation of Linear Models with Crossed Error Structure.” Journal of Econometrics 2, 67–78.įurnival, G.M. “A Note on Screening Regression Equations.” American Statistician 37, 152–155.įuller, W.A. “Influential Observations and Outliers in Regression.” Technometrics 23, 21–26.įomby, T.B., Hill, R.C. New York: John Wiley and Sons, Inc.Ĭook, R.D. Sensitivity Analysis in Linear Regression. “A Simple Test for Heteroscedasticity and Random Coefficient Variation.” Econometrica 47, 1287–1294.Ĭhatterjee, S. New York: John Wiley and Sons, Inc.īreusch, T. Statistics For Experimenters: An Introduction to Design, Data Analysis and Model Building. “Tolerance and Condition in Regression Computations.” Journal of the American Statistical Association 72, 46–53.īox, G.E.P., Hunter, W.G. “Demeaning Conditioning Diagnostics Through Centering (with Discussion).” The American Statistician 38, 73–77.īerk, K.N. New York: John Wiley and Sons, Inc.īelsey, D.A. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Oxford: Clarendon Press.īelsey, D.A., Kuh, E., and Welsch, R.E. “Finding the Outliers that Matter.” Journal of the Royal Statistical Society, Series B 40, 85–93.Ītkinson, A.C. This process is experimental and the keywords may be updated as the learning algorithm improves.Īndrews, D.F. These keywords were added by machine and not by the authors. A data set to be used as a multiple regression example is described next. In this chapter, an extensive outline of the multiple linear regression model and its applications will be presented. In a multivariate setting, the regression model can be extended so that Y can be related to a set of p explanatory variables X 1, X 2, …, X p. It was shown that by extending the regression to include the explanatory variable Z, the relationship between Y and X can be studied while controlling or taking into account Z. In the latter part of Chapter 3, the impact of another explanatory variable Z on the regression relationship between X and Y was also studied. In Chapter 3 the concept of a regression model was introduced to study the relationship between two quantitative variables X and Y. The multiple linear regression model is the most commonly applied statistical technique for relating a set of two or more variables.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |