# Partial residual plot

> Mediated Wiki article. Canonical URL: https://mediated.wiki/source/Partial_residual_plot
> Markdown URL: https://mediated.wiki/source/Partial_residual_plot.md
> Source: https://en.wikipedia.org/wiki/Partial_residual_plot
> Source revision: 1142292871
> License: Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/)

Graphical technique in statistics to show error in a model

This article includes a list of references, related reading, or external links, but its sources remain unclear because it lacks inline citations. Please help improve this article by introducing more precise citations. (February 2022) (Learn how and when to remove this message)

In [applied statistics](/source/Applied_statistics), a **partial residual plot** is a [graphical technique](/source/Graphical_technique) that attempts to show the relationship between a given [independent variable](/source/Independent_variable) and the [response variable](/source/Response_variable) given that other independent variables are also in the [model](/source/Statistical_model).

## Background

When performing a [linear regression](/source/Linear_regression) with a single [independent variable](/source/Independent_variable), a [scatter plot](/source/Scatter_plot) of the [response variable](/source/Response_variable) against the independent variable provides a good indication of the nature of the relationship. If there is more than one independent variable, things become more complicated. Although it can still be useful to generate scatter plots of the response variable against each of the independent variables, this does not take into account the effect of the other independent variables in the model.

## Definition

Partial residual plots are formed as

- Residuals + β ^ i X i versus X i , {\displaystyle {\text{Residuals}}+{\hat {\beta }}_{i}X_{i}{\text{ versus }}X_{i},}

where

- Residuals = [residuals](/source/Errors_and_residuals_in_statistics) from the [full model](https://en.wikipedia.org/w/index.php?title=Full_model&action=edit&redlink=1),

- β ^ i {\displaystyle {\hat {\beta }}_{i}} = regression coefficient from the *i*-th independent variable in the full model,

- *X*i = the *i*-th independent variable.

Partial residual plots are widely discussed in the regression diagnostics literature (e.g., see the References section below). Although they can often be useful, they can also fail to indicate the proper relationship. In particular, if *X*i is highly correlated with any of the other independent variables, the variance indicated by the partial residual plot can be much less than the actual variance. These issues are discussed in more detail in the references given below.

## CCPR plot

The CCPR (component and component-plus-residual) plot is a refinement of the partial residual plot, adding

- β ^ i X i v e r s u s X i . {\displaystyle {\hat {\beta }}_{i}X_{i}\mathrm {\ versus\ } X_{i}.}

This is the "component" part of the plot and is intended to show where the "fitted line" would lie.

## See also

- [Partial regression plot](/source/Partial_regression_plot)

- [Partial leverage plot](/source/Partial_leverage_plot)

- [Variance inflation factors](/source/Variance_inflation_factor) for a multi-linear fit.

## References

- Tom Ryan (1997). *Modern Regression Methods*. John Wiley.

- Neter, Wasserman, and Kutner (1990). *Applied Linear Statistical Models* (3rd ed.). Irwin.{{[cite book](https://en.wikipedia.org/wiki/Template:Cite_book)}}: CS1 maint: multiple names: authors list ([link](https://en.wikipedia.org/wiki/Category:CS1_maint:_multiple_names:_authors_list))

- Draper and Smith (1998). *Applied Regression Analysis* (3rd ed.). John Wiley.

- Cook and Weisberg (1982). *Residuals and Influence in Regression*. Chapman and Hall.

- Belsley, Kuh, and Welsch (1980). *Regression Diagnostics*. John Wiley.{{[cite book](https://en.wikipedia.org/wiki/Template:Cite_book)}}: CS1 maint: multiple names: authors list ([link](https://en.wikipedia.org/wiki/Category:CS1_maint:_multiple_names:_authors_list))

- Paul Velleman; Roy Welsch (November 1981). "Efficient Computing of Regression Diagnostics". *The American Statistician*. **35** (4). American Statistical Association: 234–242. [doi](/source/Doi_(identifier)):[10.2307/2683296](https://doi.org/10.2307%2F2683296). [JSTOR](/source/JSTOR_(identifier)) [2683296](https://www.jstor.org/stable/2683296).

- Chatterjee, Samprit; Hadi, Ali S. (2009). [*Sensitivity Analysis in Linear Regression*](https://books.google.com/books?id=qozaTQMdC0EC&pg=PA54). John Wiley & Sons. pp. 54–59. [ISBN](/source/ISBN_(identifier)) [9780470317426](https://en.wikipedia.org/wiki/Special:BookSources/9780470317426).

## External links

- [Partial Residual Plot](http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/partresi.htm)

This article incorporates [public domain material](/source/Copyright_status_of_works_by_the_federal_government_of_the_United_States) from the [National Institute of Standards and Technology](https://www.nist.gov)

---
Adapted from the Wikipedia article [Partial residual plot](https://en.wikipedia.org/wiki/Partial_residual_plot) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Partial_residual_plot?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.