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Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Regression diagnostic
- No single diagnostic validates a model; residual patterns, leverage, influence, functional form, variance, dependence and distribution require distinct checks and multiplicity and exploratory reuse complicate formal p-values.
This sourceA regression diagnostic may take the form of a graphical result, informal quantitative results or a formal statistical hypothesis test, each of which provides guidance for further stages of a regression analysis. ==Introduction== Regression diagnostics have often been developed or were initially proposed in the context of linear regression or, more particularly, ordinary least squares. This means that many formally defined diagnostics are only available for these contexts. ==Assessing assumptions== ;Distribution of model errors Normal probability plot ;Homoscedasticity Goldfeld–Quandt test Breusch–Pagan test Park test White test ;Correlation of model errors Breusch–Godfrey test ==Assessing model structure== ;Adequacy of existing explanatory variables Partial residual plot Ramsey RESET test F test for use when there are replicated observations, so that a comparison can be made between the lack-of-fit sum of squares and the pure error sum of squares, under the assumption that model errors are homoscedastic and have a normal distribution. ;Adding or dropping explanatory variables Partial regression plot Student's t test for testing inclusion of a single explanatory variable, or the F test for testing inclusion of a group of variables, both under the assumption that model errors are homoscedastic and have a normal distribution. ;Change of model structure between groups of observations Structural break test **Chow test ;Comparing model structures PRESS statistic ==Important groups of observations== ;Outliers ;Influential observations Leverage (statistics), partial leverage DFFITS Cook's distance ==References== Everitt, B.S. (2002) The Cambridge Dictionary of Statistics, CUP. (entry for Regression diagnostics).
- No single diagnostic validates a model; residual patterns, leverage, influence, functional form, variance, dependence and distribution require distinct checks and multiplicity and exploratory reuse complicate formal p-values.
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