Regression diagnostic

D.A. Belsley, E.Kuh, R.E. Welsch, Regression diagnostics. Identifying influential data and sources of collinearity, John Wiley Sons, New York, 1980. 

After deleting the outlying observations from one particular gene, regression diagnostics show that, for the remaining 1285 genes, the reduced model... 

Regression analysis includes not only the estimation of model regression parameters, but also the calculation of goodness of fit and -> goodness of prediction statistics, regression diagnostics, residual analysis, and influence analysis [Atkinson, 1985]. 

In particular, the leverage matrix H, also called influence matrix, is an important tool in regression diagnostics, containing information on the independent variables on which the model is built. 

Belsley, D.A., E. Kuth, and R.W. Welsch. 1980. Regression Diagnostics. John Wiley Sons, New York. 

Unit cell refinement from powder diffraction data the use of regression diagnostics, T. J. B. 

Belsley DA, Kuh E, Welsch RE, Regression Diagnostics Identifying Influential Data and Sources of Collinearity, John Wiley and Sons, Inc., New York, 1980. 

Previously in this chapter, we discussed Cook s distance for simple linear regression, a regression diagnostic that is used to detect an extreme value and its influence by removing it from the analysis and then observing the results. In multiple linear regression, the same approach is used, except that a data set is removed. Cook s distance lets the researcher determine just how influential the ith value set is. 

To understand the usage of the analysis of variance and regression diagnostics... 

Apart from the analysis of residuals, the recognition of outUers and of influential observations is important for the selection of a regression model. We will raise those questions for the generalized regression diagnostics in Section 6.2. 

As an example of the application of these methods, spectro-metric multicomponent analysis will be considered, leading to an introduction to regression diagnostics in multiple linear regression. 

Figure 6.10 Regression diagnostics Residual plot in dependence on for influential observations and out- the calculated y values, (c) Jack-liers. (a) Cook s distance for recogni- knifed residuals according to tion of influential observations, (b) Eq. (6.102).

For all three methods the final model selected like any regression model should be evaluated to the regression diagnostics described earlier. 

D. A. Belsley, E. Kuh, and R. E. Welsch, Regression Diagnostics, Identifying Influential Data... 

Kleinbaum, D. G., Kupper, L. L. and Muller, K. E. 1988. Applied Regression Analysis and Other Multivariable Methods, 3rd edn, Wadsworth, Boston, MA, USA. (Another classic in its field with a good treatment of regression diagnostics.)... 

Quantitative structure-activity relationships, acute toxicity, mammalian toxicity, LD50, LC50, aquatic toxicity, regression, regression diagnostics, animal usage reduction, interspecies extrapolation. 

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