Scatterplots for Logistic Regression
We present a method for graphically displaying regression data with Bernoulli responses. The method, which is based on the use of grayscale graphics to visualize contributions to a likelihood function, provides an analog of a scatterplot for logistic regression, as well as probit analysis. Furthermo...
Gespeichert in:
| Veröffentlicht in: | Journal of computational and graphical statistics 1999-09, Vol.8 (3), p.413-425 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 425 |
|---|---|
| container_issue | 3 |
| container_start_page | 413 |
| container_title | Journal of computational and graphical statistics |
| container_volume | 8 |
| creator | Eno, Daniel R. Terrell, George R. |
| description | We present a method for graphically displaying regression data with Bernoulli responses. The method, which is based on the use of grayscale graphics to visualize contributions to a likelihood function, provides an analog of a scatterplot for logistic regression, as well as probit analysis. Furthermore, the method may be used in place of a traditional scatterplot in situations where such plots are often used. |
| doi_str_mv | 10.1080/10618600.1999.10474822 |
| format | Article |
| fullrecord | <record><control><sourceid>jstor_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1080_10618600_1999_10474822</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>1390865</jstor_id><sourcerecordid>1390865</sourcerecordid><originalsourceid>FETCH-LOGICAL-c287t-3e292f503ce6d20107a36d6d0b26151899782ae96a2e450cfd7d8045ad7649a43</originalsourceid><addsrcrecordid>eNqFj8tKAzEUhoMoWKuvILPodvQkmdyWpXiDAcHLOsQkU6ZMJyUJSN_eDGPBnatz4fvP4UPoFsMdBgn3GDiWHMqklCqrRjSSkDO0wIyKmgjMzktfoHqiLtFVSjsAwFyJBVq9W5Ozj4ch5FR1IVZt2PYp97Z689voU-rDeI0uOjMkf_Nbl-jz8eFj81y3r08vm3VbWyJFrqkninQMqPXcEcAgDOWOO_giHDMslRKSGK-4Ib5hYDsnnISGGSd4o0xDl4jPd20MKUXf6UPs9yYeNQY9ueqTq55c9cm1BFdzcJdyiH9ThILQmCqQnBVsPWP9WEz35jvEwelsjkOIXTSj7ZOm_7z6AcilZTE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Scatterplots for Logistic Regression</title><source>JSTOR Mathematics & Statistics</source><source>JSTOR Archive Collection A-Z Listing</source><creator>Eno, Daniel R. ; Terrell, George R.</creator><creatorcontrib>Eno, Daniel R. ; Terrell, George R.</creatorcontrib><description>We present a method for graphically displaying regression data with Bernoulli responses. The method, which is based on the use of grayscale graphics to visualize contributions to a likelihood function, provides an analog of a scatterplot for logistic regression, as well as probit analysis. Furthermore, the method may be used in place of a traditional scatterplot in situations where such plots are often used.</description><identifier>ISSN: 1061-8600</identifier><identifier>EISSN: 1537-2715</identifier><identifier>DOI: 10.1080/10618600.1999.10474822</identifier><language>eng</language><publisher>Taylor & Francis Group</publisher><subject>Binomials ; Contour lines ; Datasets ; Discussion Article ; Gray scale ; Grayscale ; Likelihood ; Linear regression ; Logistic regression ; Maximum likelihood estimation ; Maximum likelihood estimators ; Poisson regression ; Probit ; Regression analysis ; Sample size</subject><ispartof>Journal of computational and graphical statistics, 1999-09, Vol.8 (3), p.413-425</ispartof><rights>Copyright Taylor & Francis Group, LLC 1999</rights><rights>Copyright 1999 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c287t-3e292f503ce6d20107a36d6d0b26151899782ae96a2e450cfd7d8045ad7649a43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/1390865$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/1390865$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,27924,27925,58017,58021,58250,58254</link.rule.ids></links><search><creatorcontrib>Eno, Daniel R.</creatorcontrib><creatorcontrib>Terrell, George R.</creatorcontrib><title>Scatterplots for Logistic Regression</title><title>Journal of computational and graphical statistics</title><description>We present a method for graphically displaying regression data with Bernoulli responses. The method, which is based on the use of grayscale graphics to visualize contributions to a likelihood function, provides an analog of a scatterplot for logistic regression, as well as probit analysis. Furthermore, the method may be used in place of a traditional scatterplot in situations where such plots are often used.</description><subject>Binomials</subject><subject>Contour lines</subject><subject>Datasets</subject><subject>Discussion Article</subject><subject>Gray scale</subject><subject>Grayscale</subject><subject>Likelihood</subject><subject>Linear regression</subject><subject>Logistic regression</subject><subject>Maximum likelihood estimation</subject><subject>Maximum likelihood estimators</subject><subject>Poisson regression</subject><subject>Probit</subject><subject>Regression analysis</subject><subject>Sample size</subject><issn>1061-8600</issn><issn>1537-2715</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><recordid>eNqFj8tKAzEUhoMoWKuvILPodvQkmdyWpXiDAcHLOsQkU6ZMJyUJSN_eDGPBnatz4fvP4UPoFsMdBgn3GDiWHMqklCqrRjSSkDO0wIyKmgjMzktfoHqiLtFVSjsAwFyJBVq9W5Ozj4ch5FR1IVZt2PYp97Z689voU-rDeI0uOjMkf_Nbl-jz8eFj81y3r08vm3VbWyJFrqkninQMqPXcEcAgDOWOO_giHDMslRKSGK-4Ib5hYDsnnISGGSd4o0xDl4jPd20MKUXf6UPs9yYeNQY9ueqTq55c9cm1BFdzcJdyiH9ThILQmCqQnBVsPWP9WEz35jvEwelsjkOIXTSj7ZOm_7z6AcilZTE</recordid><startdate>19990901</startdate><enddate>19990901</enddate><creator>Eno, Daniel R.</creator><creator>Terrell, George R.</creator><general>Taylor & Francis Group</general><general>American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>19990901</creationdate><title>Scatterplots for Logistic Regression</title><author>Eno, Daniel R. ; Terrell, George R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c287t-3e292f503ce6d20107a36d6d0b26151899782ae96a2e450cfd7d8045ad7649a43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Binomials</topic><topic>Contour lines</topic><topic>Datasets</topic><topic>Discussion Article</topic><topic>Gray scale</topic><topic>Grayscale</topic><topic>Likelihood</topic><topic>Linear regression</topic><topic>Logistic regression</topic><topic>Maximum likelihood estimation</topic><topic>Maximum likelihood estimators</topic><topic>Poisson regression</topic><topic>Probit</topic><topic>Regression analysis</topic><topic>Sample size</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Eno, Daniel R.</creatorcontrib><creatorcontrib>Terrell, George R.</creatorcontrib><collection>CrossRef</collection><jtitle>Journal of computational and graphical statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eno, Daniel R.</au><au>Terrell, George R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Scatterplots for Logistic Regression</atitle><jtitle>Journal of computational and graphical statistics</jtitle><date>1999-09-01</date><risdate>1999</risdate><volume>8</volume><issue>3</issue><spage>413</spage><epage>425</epage><pages>413-425</pages><issn>1061-8600</issn><eissn>1537-2715</eissn><abstract>We present a method for graphically displaying regression data with Bernoulli responses. The method, which is based on the use of grayscale graphics to visualize contributions to a likelihood function, provides an analog of a scatterplot for logistic regression, as well as probit analysis. Furthermore, the method may be used in place of a traditional scatterplot in situations where such plots are often used.</abstract><pub>Taylor & Francis Group</pub><doi>10.1080/10618600.1999.10474822</doi><tpages>13</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1061-8600 |
| ispartof | Journal of computational and graphical statistics, 1999-09, Vol.8 (3), p.413-425 |
| issn | 1061-8600 1537-2715 |
| language | eng |
| recordid | cdi_crossref_primary_10_1080_10618600_1999_10474822 |
| source | JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing |
| subjects | Binomials Contour lines Datasets Discussion Article Gray scale Grayscale Likelihood Linear regression Logistic regression Maximum likelihood estimation Maximum likelihood estimators Poisson regression Probit Regression analysis Sample size |
| title | Scatterplots for Logistic Regression |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T17%3A57%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Scatterplots%20for%20Logistic%20Regression&rft.jtitle=Journal%20of%20computational%20and%20graphical%20statistics&rft.au=Eno,%20Daniel%20R.&rft.date=1999-09-01&rft.volume=8&rft.issue=3&rft.spage=413&rft.epage=425&rft.pages=413-425&rft.issn=1061-8600&rft.eissn=1537-2715&rft_id=info:doi/10.1080/10618600.1999.10474822&rft_dat=%3Cjstor_cross%3E1390865%3C/jstor_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_jstor_id=1390865&rfr_iscdi=true |