{"id":"https://openalex.org/W4288074684","doi":"https://doi.org/10.1080/03610918.2022.2103566","title":"Tests of equality and equivalence of regression slopes: R Shiny apps for optimal sample sizes","display_name":"Tests of equality and equivalence of regression slopes: R Shiny apps for optimal sample sizes","publication_year":2022,"publication_date":"2022-07-27","ids":{"openalex":"https://openalex.org/W4288074684","doi":"https://doi.org/10.1080/03610918.2022.2103566"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2022.2103566","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2103566","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","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/A5018212350","display_name":"Wei\u2010Ming Luh","orcid":"https://orcid.org/0000-0002-7043-9188"},"institutions":[{"id":"https://openalex.org/I91807558","display_name":"National Cheng Kung University","ror":"https://ror.org/01b8kcc49","country_code":"TW","type":"education","lineage":["https://openalex.org/I91807558"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Wei-ming Luh","raw_affiliation_strings":["Institute of Education, National Cheng Kung University, Tainan, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-7043-9188","affiliations":[{"raw_affiliation_string":"Institute of Education, National Cheng Kung University, Tainan, Taiwan","institution_ids":["https://openalex.org/I91807558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5018212350"],"corresponding_institution_ids":["https://openalex.org/I91807558"],"apc_list":null,"apc_paid":null,"fwci":0.2328,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.55489287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"53","issue":"7","first_page":"3352","last_page":"3367"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9854999780654907,"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":0.9854999780654907,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9699000120162964,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9606000185012817,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.8172620534896851},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.7743873596191406},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6344782710075378},{"id":"https://openalex.org/keywords/statistical-power","display_name":"Statistical power","score":0.6120449900627136},{"id":"https://openalex.org/keywords/type-i-and-type-ii-errors","display_name":"Type I and type II errors","score":0.6021392345428467},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5519282221794128},{"id":"https://openalex.org/keywords/sample-variance","display_name":"Sample variance","score":0.5297363996505737},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5252910852432251},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5246046781539917},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5018291473388672},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.4744901657104492},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.46886521577835083},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4543078541755676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.41719380021095276},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33001893758773804},{"id":"https://openalex.org/keywords/discrete-mathematics","display_name":"Discrete mathematics","score":0.08538571000099182}],"concepts":[{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.8172620534896851},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.7743873596191406},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6344782710075378},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.6120449900627136},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.6021392345428467},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5519282221794128},{"id":"https://openalex.org/C2993021520","wikidata":"https://www.wikidata.org/wiki/Q175199","display_name":"Sample variance","level":3,"score":0.5297363996505737},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5252910852432251},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5246046781539917},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5018291473388672},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.4744901657104492},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.46886521577835083},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4543078541755676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.41719380021095276},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33001893758773804},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.08538571000099182},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2022.2103566","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2022.2103566","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G54909275","display_name":null,"funder_award_id":"NSC104-2410-H-006-015-My3","funder_id":"https://openalex.org/F4320321040","funder_display_name":"National Science Council"}],"funders":[{"id":"https://openalex.org/F4320321040","display_name":"National Science Council","ror":"https://ror.org/02kv4zf79"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W114686790","https://openalex.org/W588037209","https://openalex.org/W632986127","https://openalex.org/W1482252402","https://openalex.org/W1906197993","https://openalex.org/W1964074524","https://openalex.org/W1964429631","https://openalex.org/W1967820379","https://openalex.org/W1976841572","https://openalex.org/W1983191206","https://openalex.org/W1997539987","https://openalex.org/W2003087668","https://openalex.org/W2014076103","https://openalex.org/W2018758420","https://openalex.org/W2025822284","https://openalex.org/W2037698344","https://openalex.org/W2039625867","https://openalex.org/W2043109051","https://openalex.org/W2043716119","https://openalex.org/W2049733617","https://openalex.org/W2062337042","https://openalex.org/W2066917628","https://openalex.org/W2068519612","https://openalex.org/W2075806409","https://openalex.org/W2077459813","https://openalex.org/W2081874232","https://openalex.org/W2086337008","https://openalex.org/W2091283050","https://openalex.org/W2094193410","https://openalex.org/W2096294711","https://openalex.org/W2108038333","https://openalex.org/W2108457450","https://openalex.org/W2114584570","https://openalex.org/W2118450528","https://openalex.org/W2123362488","https://openalex.org/W2131969404","https://openalex.org/W2147453331","https://openalex.org/W2155164874","https://openalex.org/W2161649600","https://openalex.org/W2201838105","https://openalex.org/W2328067344","https://openalex.org/W2400912040","https://openalex.org/W2414172611","https://openalex.org/W2478201239","https://openalex.org/W2480935928","https://openalex.org/W2535332158","https://openalex.org/W2605098691","https://openalex.org/W2788930055","https://openalex.org/W2800755150","https://openalex.org/W2901859784","https://openalex.org/W2970146707","https://openalex.org/W2971276692","https://openalex.org/W4237084078","https://openalex.org/W4242048439","https://openalex.org/W4247367216","https://openalex.org/W4283730883","https://openalex.org/W6620336716","https://openalex.org/W6845883598"],"related_works":["https://openalex.org/W3003558595","https://openalex.org/W84084013","https://openalex.org/W3137029931","https://openalex.org/W2883778230","https://openalex.org/W3088276941","https://openalex.org/W2133619163","https://openalex.org/W2083241154","https://openalex.org/W154531455","https://openalex.org/W2030905385","https://openalex.org/W4249473446"],"abstract_inverted_index":{"The":[0,72,85,109,130,145],"task":[1,18],"of":[2,23,79,92,127,132],"comparing":[3],"regression":[4],"slopes":[5,80],"is":[6,74,87,107,148],"very":[7],"common":[8],"in":[9],"practice.":[10],"However,":[11],"the":[12,17,27,33,40,54,64,77,90,128,133,155],"sample":[13,46,66],"size":[14,47,165],"needed":[15],"for":[16,36,48,51,62,75,88,99,139,176],"suffers":[19],"from":[20],"a":[21,44,151],"lack":[22],"statistical":[24,101,162],"power":[25,102],"and":[26,39,81,124,142,161,171],"heterogeneous":[28],"variance":[29],"problem.":[30],"Furthermore,":[31],"considering":[32],"different":[34,45,120,125],"costs":[35],"each":[37,49],"group":[38,50],"need":[41],"to":[42,58,153],"allocate":[43],"optimization":[52],"purposes,":[53],"present":[55],"study":[56],"aims":[57],"provide":[59],"innovative":[60],"solutions":[61],"calculating":[63],"optimal":[65],"sizes":[67],"by":[68],"using":[69,150],"two":[70,167],"tests.":[71],"first":[73],"testing":[76,89],"equality":[78],"constructing":[82],"CIs":[83],"simultaneously.":[84],"second":[86],"equivalence":[91],"slopes.":[93],"An":[94],"exhaustive":[95],"local":[96],"search":[97],"algorithm":[98],"maximal":[100],"or":[103],"minimal":[104],"sampling":[105,121],"cost":[106],"provided.":[108],"proposed":[110,146],"method":[111,134,147],"incorporates":[112],"profound":[113],"factors":[114],"such":[115],"as":[116],"unequal":[117],"error":[118,159],"variances,":[119],"unit":[122],"costs,":[123],"variances":[126],"predictor.":[129],"application":[131],"can":[135],"therefore":[136],"be":[137],"comprehensive":[138],"both":[140],"experimental":[141],"non-experimental":[143],"studies.":[144],"validated":[149],"simulation":[152],"obtain":[154],"empirical":[156],"Type":[157],"I":[158],"rate":[160],"power.":[163],"Sample":[164],"tables,":[166],"R":[168,172],"Shiny":[169],"apps,":[170],"codes":[173],"are":[174],"provided":[175],"easy":[177],"use.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}