Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.
 

Comparison of mean square error of prediction or estimation of a full model and a subset model in linear regression

Bingham, Christopher
Cook, R. Dennis
Weisberg, Sanford
1978-03
Thumbnail Image

View/Download File

Technical Report 308 Comparison of mean square error of predicion or estimation of a full model and a subset model in linear regression Bingham, Cook, and Weisberg.pdf (505.13 KB)

Persistent link to this item

https://hdl.handle.net/11299/199305
Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Comparison of mean square error of prediction or estimation of a full model and a subset model in linear regression

Authors

Published Date

1978-03

Publisher

University of Minnesota

Type

Technical Report

Abstract

Keywords

Description

1 online resource (PDF, 17 pages)

Related to

Replaces

License

Collections

School of Statistics Technical Reports

Series/Report Number

Technical report; No. 308

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Other identifiers

Suggested citation

Bingham, Christopher; Cook, R. Dennis; Weisberg, Sanford. (1978). Comparison of mean square error of prediction or estimation of a full model and a subset model in linear regression. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/199305.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.