{"id":"https://openalex.org/W4252436620","doi":"https://doi.org/10.1002/9781119212515.ch7","title":"Strategies for Assessing the Adequacy of the Linear Regression Model","display_name":"Strategies for Assessing the Adequacy of the Linear Regression Model","publication_year":2017,"publication_date":"2017-02-11","ids":{"openalex":"https://openalex.org/W4252436620","doi":"https://doi.org/10.1002/9781119212515.ch7"},"language":"en","primary_location":{"id":"doi:10.1002/9781119212515.ch7","doi":"https://doi.org/10.1002/9781119212515.ch7","is_oa":false,"landing_page_url":"https://doi.org/10.1002/9781119212515.ch7","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applications of Regression Models in Epidemiology","raw_type":"other"},"volume":{"id":"https://openalex.org/W2587255012","display_name":"Applications of Regression Models in Epidemiology"},"type":"other","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053444976","display_name":"Erick L. Suarez","orcid":"https://orcid.org/0009-0007-2451-9527","observed_orcids":["https://orcid.org/0009-0007-2451-9527"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erick Su\u00e1rez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071829791","display_name":"Cynthia M. P\u00e9rez","orcid":"https://orcid.org/0000-0003-2529-7042","observed_orcids":["https://orcid.org/0000-0003-2529-7042"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cynthia M. P\u00e9rez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Roberto Rivera","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roberto Rivera","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5048314699","display_name":"Melissa N. Mart\u00ednez","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Melissa N. Mart\u00ednez","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"97","last_page":"116"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multiple-linear-regression","display_name":"multiple linear regression","score":0.9419999718666077},{"id":"https://openalex.org/keywords/model-adequacy-assessment","display_name":"model adequacy assessment","score":0.7950000166893005},{"id":"https://openalex.org/keywords/residual-analysis","display_name":"residual analysis","score":0.7900000214576721},{"id":"https://openalex.org/keywords/financial-leverage","display_name":"leverage","score":0.7310000061988831},{"id":"https://openalex.org/keywords/influential-observations","display_name":"influential observations","score":0.7250000238418579},{"id":"https://openalex.org/keywords/outliers","display_name":"outliers","score":0.9639999866485596},{"id":"https://openalex.org/keywords/cook-s-distance","display_name":"Cook's distance","score":0.9570000171661377},{"id":"https://openalex.org/keywords/dffits","display_name":"DFFITS","score":0.7540000081062317},{"id":"https://openalex.org/keywords/regression-assumptions","display_name":"regression assumptions","score":0.5339999794960022},{"id":"https://openalex.org/keywords/residual-plots","display_name":"residual plots","score":0.35600000619888306}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7965041399002075},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.6787045001983643},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.649356484413147},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5565521121025085},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5373405814170837},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.5333430767059326},{"id":"https://openalex.org/C175615203","wikidata":"https://www.wikidata.org/wiki/Q6143358","display_name":"Studentized residual","level":2,"score":0.4901893734931946},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4858098030090332},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.4808152914047241},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4576287865638733},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.45657825469970703},{"id":"https://openalex.org/C32224588","wikidata":"https://www.wikidata.org/wiki/Q7250175","display_name":"Proper linear model","level":4,"score":0.4523157477378845},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44497150182724},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.44152578711509705},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.4394114017486572},{"id":"https://openalex.org/C57381214","wikidata":"https://www.wikidata.org/wiki/Q55631393","display_name":"Regression diagnostic","level":4,"score":0.4343448579311371},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42630061507225037},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41533809900283813},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4104310870170593},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4008723497390747},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.20951929688453674},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.14551663398742676},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/9781119212515.ch7","doi":"https://doi.org/10.1002/9781119212515.ch7","is_oa":false,"landing_page_url":"https://doi.org/10.1002/9781119212515.ch7","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applications of Regression Models in Epidemiology","raw_type":"other"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2171033594","https://openalex.org/W2502759836","https://openalex.org/W4210772173"],"related_works":["https://openalex.org/W4280609547","https://openalex.org/W4404692938","https://openalex.org/W1524053130","https://openalex.org/W4206963121","https://openalex.org/W1539346743","https://openalex.org/W2744518040","https://openalex.org/W4233091560","https://openalex.org/W3128588546","https://openalex.org/W2900194739","https://openalex.org/W2343335981"],"abstract_inverted_index":{"This":[0],"chapter":[1,50,83],"introduces":[2],"some":[3],"of":[4,20,31,53,56,73,105,114,123],"the":[5,18,21,46,54,65,71,103,112,117,121],"procedures":[6,100],"and":[7,12,41,62,76,86,120],"statistics":[8],"for":[9,37,101],"evaluating":[10],"residuals":[11,32,115],"operational":[13],"criteria":[14],"to":[15,35,110,129,134],"determine":[16,135],"if":[17],"assumptions":[19],"multiple":[22],"linear":[23,94],"regression":[24,74,95],"model":[25,47],"(MLRM)":[26],"are":[27,33,98,140],"met.":[28],"Different":[29],"types":[30],"defined":[34],"compensate":[36],"differences":[38],"in":[39,44,89],"leverage":[40],"other":[42],"issues":[43],"meeting":[45],"assumptions.":[48],"The":[49,82,107,125],"gives":[51],"examples":[52],"values":[55,88,119],"leverages,":[57],"Cook's":[58],"distance,":[59],"COVRATIO,":[60],"DFBETAS,":[61],"DFFITS":[63],"with":[64],"data":[66,91],"set.":[67,92],"Outliers":[68],"can":[69],"increase":[70],"variance":[72],"coefficients":[75],"hence":[77],"make":[78],"inference":[79],"less":[80],"reliable.":[81],"identifies":[84],"outliers":[85],"influential":[87],"a":[90,131,141],"In":[93],"analysis,":[96],"there":[97],"two":[99],"determining":[102],"presence":[104],"outliers.":[106],"first":[108],"is":[109,128],"observe":[111],"graph":[113,122],"versus":[116],"fitted":[118],"normality.":[124],"second":[126],"method":[127],"compute":[130],"numerical":[132],"expression":[133],"whether":[136],"or":[137],"not":[138],"observations":[139],"typical.":[142]},"counts_by_year":[],"updated_date":"2026-10-10T09:11:00.788812","created_date":"2022-05-12T00:00:00"}
