{"id":"https://openalex.org/W1994532591","doi":"https://doi.org/10.1080/00401706.1999.10485642","title":"Introduction to Design and Analysis of Experiments","display_name":"Introduction to Design and Analysis of Experiments","publication_year":1999,"publication_date":"1999-05-01","ids":{"openalex":"https://openalex.org/W1994532591","doi":"https://doi.org/10.1080/00401706.1999.10485642","mag":"1994532591"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.1999.10485642","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.1999.10485642","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","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/A5022001444","display_name":"Richard O. Lynch","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Richard O. Lynch","raw_affiliation_strings":["Lynch Statistical Consulting"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lynch Statistical Consulting","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5022001444"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4816,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.66215029,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"41","issue":"2","first_page":"170","last_page":"170"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.8644000291824341,"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"}},"topics":[{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.8644000291824341,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/notation","display_name":"Notation","score":0.68260657787323},{"id":"https://openalex.org/keywords/main-effect","display_name":"Main effect","score":0.6744913458824158},{"id":"https://openalex.org/keywords/factorial-experiment","display_name":"Factorial experiment","score":0.6265605092048645},{"id":"https://openalex.org/keywords/factorial","display_name":"Factorial","score":0.5861541032791138},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5765552520751953},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.499401330947876},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.417098730802536},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3824479579925537},{"id":"https://openalex.org/keywords/arithmetic","display_name":"Arithmetic","score":0.2750470042228699}],"concepts":[{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.68260657787323},{"id":"https://openalex.org/C28662235","wikidata":"https://www.wikidata.org/wiki/Q6736213","display_name":"Main effect","level":2,"score":0.6744913458824158},{"id":"https://openalex.org/C169222746","wikidata":"https://www.wikidata.org/wiki/Q4116558","display_name":"Factorial experiment","level":2,"score":0.6265605092048645},{"id":"https://openalex.org/C183763347","wikidata":"https://www.wikidata.org/wiki/Q120976","display_name":"Factorial","level":2,"score":0.5861541032791138},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5765552520751953},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.499401330947876},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.417098730802536},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3824479579925537},{"id":"https://openalex.org/C94375191","wikidata":"https://www.wikidata.org/wiki/Q11205","display_name":"Arithmetic","level":1,"score":0.2750470042228699},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/00401706.1999.10485642","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.1999.10485642","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","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":0,"referenced_works":[],"related_works":["https://openalex.org/W4245601516","https://openalex.org/W4229629925","https://openalex.org/W1600342170","https://openalex.org/W4230472231","https://openalex.org/W2321613971","https://openalex.org/W1570886121","https://openalex.org/W2002931749","https://openalex.org/W1633163079","https://openalex.org/W2341910357","https://openalex.org/W3203873370"],"abstract_inverted_index":{"To":[0,6],"the":[1,7,36,77,84,97,121,127,136,151,197,206,216,224,236,249,258,291,300,329,337,374,517,520,558],"Instructor.":[2],"Sample":[3],"Exam":[4],"Questions.":[5],"Student.":[8],"Acknowledgments.":[9],"1.":[10,15,46,95,141,155,202,246,304,335,368,389,423,457,502,522],"Introduction":[11],"to":[12,103,325,420,495,530],"Experimental":[13,453],"Design.":[14,154],"The":[16,32,53,142,156,242,263,369,415,433,523],"Challenge":[17],"of":[18,35,49,130,138,176,199,244,273,348,361,500,511,519,525,539],"planning":[19],"a":[20,67,184,229,284,312,365],"good":[21],"experiment.":[22],"2.":[23,40,52,99,144,163,213,254,310,342,377,395,427,466,504,528],"Three":[24,343],"basic":[25,157,208,370],"principles":[26],"and":[27,43,65,86,91,150,174,196,205,215,221,248,271,306,373,386,393,403,410,418,442,455,464,487,498,507,516],"four":[28,37],"experimental":[29,38],"designs.":[30,39,181,382],"3.":[31,57,80,107,146,166,219,262,318,350,400,432,477,509,535],"factor":[33,191,459,471],"structure":[34,168,192],"Informal":[41,58,70,164,319],"Analysis":[42,360,499,510],"Checking":[44],"Assumptions.":[45,521],"What":[47],"analysis":[48,175,272],"variance":[50,177],"does.":[51],"six":[54],"fisher":[55],"assumptions.":[56],"analysis,":[59,71],"part":[60,72],"1:":[61],"parallel":[62],"dot":[63],"graphs":[64],"choosing":[66],"scale.":[68],"4.":[69,116,133,172,227,269,322,407,437,483,541],"2:":[73],"interaction":[74,217,481],"graph":[75],"for":[76,114,178,190,223,235,277,290,356,397,449,469,544,551],"log":[78],"concentrations.":[79],"Formal":[81,117],"Anova:":[82],"Decomposing":[83,96,364,378],"Data":[85,366,562],"Measuring":[87],"Variability,":[88],"Testing":[89],"Hypothesis":[90],"Estimating":[92,446],"True":[93],"Differences.":[94],"data.":[98],"Computing":[100],"mean":[101,112,485],"squares":[102],"measure":[104],"average":[105],"variability.":[106],"Standard":[108],"deviation":[109],"=":[110],"root":[111],"square":[113,260,486],"residuals.":[115],"hypothesis":[118,526],"testing:":[119],"are":[120,439,557],"effects":[122],"detectable?":[123],"5.":[124,148,182,232,275,359,445,547],"Confidence":[125,387],"intervals:":[126],"likely":[128],"size":[129],"true":[131],"differences.":[132],"Decisions":[134],"About":[135],"Content":[137],"an":[139],"Experiment.":[140],"response.":[143],"Conditions.":[145],"Material.":[147],"Randomization":[149],"Basic":[152,302,330],"Factorial":[153,200,203,333],"factorial":[158,209],"design":[159,239,252,267,308,313],"(What":[160,169],"you":[161,170],"do).":[162],"analysis.":[165],"Factor":[167],"get).":[171],"Decomposition":[173,220,270],"one-way":[179],"BF":[180,238,338],"Using":[183,228,283],"computer":[185,230],"[Optional].":[186,193,231,240,286,413],"6.":[187,194,282,553],"Algebraic":[188,233,288],"notation":[189,234,289],"Interaction":[195,214],"Principle":[198,243],"Crossing.":[201,334],"crossing":[204,463],"two-way":[207,225,237],"design,":[210],"or":[211,344],"BF[2].":[212],"graph.":[218],"ANOVA":[222],"design.":[226,376],"7.":[241,287],"Blocking.":[245],"Blocking":[247],"complete":[250],"block":[251],"(CB).":[253],"Two":[255],"nuisance":[256],"factors:":[257],"Latin":[259],"design(LS).":[261],"split":[264],"plot/repeated":[265],"measures":[266],"(SP/RM).":[268],"variance.":[274,362],"Scatterplots":[276],"data":[278,379],"sets":[279],"with":[280,299,479],"blocks.":[281],"computer.":[285],"CB,":[292],"LS":[293],"And":[294],"SP/RM":[295],"Designs.":[296,303],"8.":[297],"Working":[298],"Four":[301,536],"Comparing":[305],"recognizing":[307],"structures.":[309],"Choosing":[311],"structure:":[314],"deciding":[315],"about":[316,532],"blocking.":[317],"analysis:":[320],"examples.":[321],"Recognizing":[323],"alternative":[324],"ANOVA.":[326],"9.":[327],"Extending":[328,336],"Designs":[331,454],"by":[332,462],"design:":[339],"general":[340],"principles.":[341],"more":[345],"crossed":[346],"factors":[347,402],"interest.":[349],"Compound":[351],"within-blocks":[352,405],"factors.":[353,406],"4.Graphical":[354],"methods":[355],"3-factor":[357],"interactions.":[358],"10.":[363],"Set.":[367],"decomposition":[371],"step":[372],"BF[1]":[375],"from":[380],"balanced":[381],"11.":[383],"Comparisons,":[384],"Contrasts,":[385],"Intervals.":[388],"Comparisons:":[390],"confidence":[391],"intervals":[392],"tests.":[394],"Adjustments":[396],"multiple":[398],"comparisons.":[399],"Between-blocks":[401],"compound":[404],"Linear":[408],"estimators":[409],"orthogonal":[411],"contrasts":[412],"12.":[414],"Fisher":[416],"Assumptions":[417],"How":[419],"Check":[421],"Them.":[422],"Same":[424],"SDs":[425],"(s).":[426],"Independent":[428],"chance":[429],"errors":[430],"(I).":[431],"normality":[434],"assumption":[435],"(N).":[436],"Effects":[438],"additive":[440],"(A)":[441],"constant":[443],"(C).":[444],"replacement":[447],"values":[448],"outliers.":[450],"13.":[451],"Other":[452],"Models.":[456],"New":[458,467],"structures":[460],"built":[461],"nesting.":[465],"uses":[468],"old":[470],"structures:":[472],"fixed":[473],"versus":[474],"random":[475],"effects.":[476,482],"Models":[478],"mixed":[480],"Expected":[484],"f-ratios.":[488,552],"14.":[489],"Continuous":[490],"Carriers:":[491],"A":[492],"Visual":[493],"Approach":[494],"Regression,":[496],"Correlation":[497],"Covariance.":[501],"Regression.":[503],"Balloon":[505],"summaries":[506],"correlation.":[508],"covariance.":[512],"15.":[513],"Sampling":[514,542],"Distributions":[515],"Role":[518],"logic":[524],"testing.":[527],"Ways":[529],"think":[531],"sampling":[533,549],"distributions.":[534,540],"fundamental":[537],"families":[538],"distributions":[543,550],"linear":[545],"estimators.":[546],"Approximate":[548],"Why":[554],"(and":[555],"when)":[556],"models":[559],"reasonable?":[560],"Tables.":[561],"Sources.":[563],"Subject":[564],"Index.":[565],"Examples.":[566]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-08-03T07:22:36.454288","created_date":"2025-10-10T00:00:00"}
