{"id":"https://openalex.org/W2789971189","doi":"https://doi.org/10.1177/1473871618756584","title":"Mixed-type distribution plots","display_name":"Mixed-type distribution plots","publication_year":2018,"publication_date":"2018-02-23","ids":{"openalex":"https://openalex.org/W2789971189","doi":"https://doi.org/10.1177/1473871618756584","mag":"2789971189"},"language":"en","primary_location":{"id":"doi:10.1177/1473871618756584","is_oa":false,"landing_page_url":"https://doi.org/10.1177/1473871618756584","pdf_url":null,"source":{"id":"https://openalex.org/S55152591","display_name":"Information Visualization","issn_l":"1473-8716","issn":["1473-8716","1473-8724"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Visualization","raw_type":"journal-article"},"type":"article","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/A5061968730","display_name":"Christopher Weld","orcid":"https://orcid.org/0000-0001-5902-9738"},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]},{"id":"https://openalex.org/I267592682","display_name":"Williams (United States)","ror":"https://ror.org/007zhvp17","country_code":"US","type":"company","lineage":["https://openalex.org/I267592682"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Christopher Weld","raw_affiliation_strings":["Department of Applied Science, College of William and Mary, Williamsburg, VA, USA"],"raw_orcid":"https://orcid.org/0000-0001-5902-9738","affiliations":[{"raw_affiliation_string":"Department of Applied Science, College of William and Mary, Williamsburg, VA, USA","institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057011822","display_name":"Lawrence M. Leemis","orcid":"https://orcid.org/0000-0001-9071-985X"},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]},{"id":"https://openalex.org/I267592682","display_name":"Williams (United States)","ror":"https://ror.org/007zhvp17","country_code":"US","type":"company","lineage":["https://openalex.org/I267592682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Leemis","raw_affiliation_strings":["Department of Mathematics, College of William and Mary, Williamsburg, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, College of William and Mary, Williamsburg, VA, USA","institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5061968730"],"corresponding_institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"],"apc_list":null,"apc_paid":null,"fwci":0.1694,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.57610092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"18","issue":"3","first_page":"311","last_page":"317"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13398","display_name":"Data Analysis with R","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13398","display_name":"Data Analysis with R","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.9796000123023987,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7410300970077515},{"id":"https://openalex.org/keywords/intuition","display_name":"Intuition","score":0.7120851874351501},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.5868706703186035},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.5058605074882507},{"id":"https://openalex.org/keywords/football","display_name":"Football","score":0.47325924038887024},{"id":"https://openalex.org/keywords/plot","display_name":"Plot (graphics)","score":0.4622291028499603},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3588079810142517},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.34603482484817505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34480661153793335},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3355277478694916},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.20888233184814453},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1889152228832245},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07748609781265259}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7410300970077515},{"id":"https://openalex.org/C132010649","wikidata":"https://www.wikidata.org/wiki/Q189222","display_name":"Intuition","level":2,"score":0.7120851874351501},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.5868706703186035},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.5058605074882507},{"id":"https://openalex.org/C2778444522","wikidata":"https://www.wikidata.org/wiki/Q1081491","display_name":"Football","level":2,"score":0.47325924038887024},{"id":"https://openalex.org/C167651023","wikidata":"https://www.wikidata.org/wiki/Q1474611","display_name":"Plot (graphics)","level":2,"score":0.4622291028499603},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3588079810142517},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.34603482484817505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34480661153793335},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3355277478694916},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.20888233184814453},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1889152228832245},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07748609781265259},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/1473871618756584","is_oa":false,"landing_page_url":"https://doi.org/10.1177/1473871618756584","pdf_url":null,"source":{"id":"https://openalex.org/S55152591","display_name":"Information Visualization","issn_l":"1473-8716","issn":["1473-8716","1473-8724"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320017","host_organization_name":"SAGE Publishing","host_organization_lineage":["https://openalex.org/P4310320017"],"host_organization_lineage_names":["SAGE Publishing"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Information Visualization","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2005903772","https://openalex.org/W2015457120","https://openalex.org/W2109574129","https://openalex.org/W2119634512","https://openalex.org/W2126381813","https://openalex.org/W2316750755","https://openalex.org/W2791199946","https://openalex.org/W4210492122","https://openalex.org/W4233110100","https://openalex.org/W4253614255"],"related_works":["https://openalex.org/W1971660097","https://openalex.org/W2365990048","https://openalex.org/W2015477300","https://openalex.org/W2237606652","https://openalex.org/W1971174658","https://openalex.org/W2370570388","https://openalex.org/W2353353369","https://openalex.org/W2372339450","https://openalex.org/W2099195351","https://openalex.org/W2182692483"],"abstract_inverted_index":{"Plotting":[0],"is":[1,102,111],"among":[2],"the":[3,70,74],"most":[4],"effective":[5],"ways":[6],"to":[7,105,113],"quickly":[8],"and":[9,40,43,77,97,134],"accurately":[10,44],"describe":[11],"a":[12,27,48,56,99],"probability":[13,82],"distribution.":[14],"It":[15],"makes":[16],"often":[17],"complex":[18],"information":[19],"accessible,":[20],"enabling":[21],"intuition":[22,54],"for":[23,32,85],"respective":[24],"outcomes":[25],"at":[26],"glance.":[28],"Matters":[29],"complicate,":[30],"however,":[31],"mixed-type":[33,86],"distributions.":[34,87],"Mixed-type":[35],"distributions":[36],"contain":[37],"both":[38],"continuous":[39],"discrete":[41],"components,":[42],"portraying":[45],"those":[46],"on":[47],"single":[49],"axis":[50,101],"can":[51],"prove":[52],"difficult\u2014misleading":[53],"as":[55],"consequence":[57],"of":[58,72,80,141],"pulling":[59],"two":[60],"otherwise":[61],"disjoint":[62],"components":[63],"into":[64],"focus":[65],"together.":[66],"This":[67],"article":[68],"examines":[69],"challenges":[71],"maintaining":[73],"simple,":[75],"concise,":[76],"accurate":[78],"format":[79],"traditional":[81],"distribution":[83],"plots":[84,117],"We":[88],"illustrate":[89],"issues":[90],"arising":[91],"within":[92],"this":[93,142],"plot":[94,143],"classification":[95],"paradigm,":[96],"why":[98],"secondary":[100],"uniquely":[103],"suited":[104],"improve":[106],"its":[107],"communication.":[108],"An":[109],"algorithm":[110],"devised":[112],"consistently":[114],"scale":[115],"such":[116],"so":[118],"that":[119],"they":[120],"better":[121],"coincide":[122],"with":[123],"intuition.":[124],"National":[125],"Football":[126],"League":[127],"football":[128],"starting":[129],"field":[130],"position,":[131],"meteorological":[132],"data,":[133],"financial":[135],"instruments":[136],"provide":[137],"examples":[138],"demonstrating":[139],"effectiveness":[140],"technique.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-04T06:00:05.808912","created_date":"2025-10-10T00:00:00"}
