{"id":29176,"date":"2023-08-28T08:29:01","date_gmt":"2023-08-28T11:29:01","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/hypothesis-testing-copy\/"},"modified":"2024-12-05T15:51:53","modified_gmt":"2024-12-05T18:51:53","slug":"one-way-anova","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/et\/uhesuunaline-anova\/","title":{"rendered":"\u00dchekordne ANOVA: m\u00f5istmine, l\u00e4biviimine ja esitamine"},"content":{"rendered":"<p>Variatsioonianal\u00fc\u00fcs (ANOVA) on statistiline meetod, mida kasutatakse kahe v\u00f5i enama r\u00fchma vaheliste keskmiste v\u00f5rdlemiseks. Eelk\u00f5ige \u00fchesuunaline ANOVA on \u00fcldkasutatav meetod \u00fche pideva muutuja dispersiooni anal\u00fc\u00fcsimiseks kahe v\u00f5i enama kategoorilise r\u00fchma vahel. Seda meetodit kasutatakse laialdaselt erinevates valdkondades, sealhulgas \u00e4ri-, sotsiaal- ja loodusteadustes, et testida h\u00fcpoteese ja teha j\u00e4reldusi r\u00fchmade vaheliste erinevuste kohta. \u00dchepoolse ANOVA p\u00f5him\u00f5tete m\u00f5istmine aitab teadlastel ja andmeanal\u00fc\u00fctikutel teha statistiliste t\u00f5endite p\u00f5hjal teadlikke otsuseid. Selles artiklis selgitame \u00fcksikasjalikult \u00fchesuunalise ANOVA tehnikat ning arutame selle rakendusi, eeldusi ja muud.<\/p>\n\n\n\n<h2 id=\"h-what-is-one-way-anova\"><strong>Mis on \u00fchesuunaline ANOVA?<\/strong><\/h2>\n\n\n\n<p>\u00dchepoolne ANOVA (Analysis of Variance) on statistiline meetod, mida kasutatakse andmete r\u00fchmade keskmiste vaheliste oluliste erinevuste testimiseks. Seda kasutatakse tavaliselt eksperimentaalsetes uuringutes, et v\u00f5rrelda erinevate ravimeetodite v\u00f5i sekkumiste m\u00f5ju konkreetsele tulemusele.<\/p>\n\n\n\n<p>ANOVA p\u00f5hiidee seisneb selles, et andmete koguvariatiivsus jaotatakse kaheks komponendiks: r\u00fchmadevaheline varieeruvus (mis tuleneb ravist) ja iga r\u00fchma sisemine varieeruvus (mis tuleneb juhuslikust variatsioonist ja individuaalsetest erinevustest). ANOVA test arvutab F-statistiku, mis on r\u00fchmadevahelise variatsiooni ja r\u00fchmasisese variatsiooni suhe.<\/p>\n\n\n\n<p>Kui F-statistik on piisavalt suur ja sellega seotud p-v\u00e4\u00e4rtus on v\u00e4iksem kui eelnevalt m\u00e4\u00e4ratud olulisuse tase (nt 0,05), n\u00e4itab see, et on olemas tugevad t\u00f5endid selle kohta, et v\u00e4hemalt \u00fcks r\u00fchma keskv\u00e4\u00e4rtus erineb oluliselt teistest. Sellisel juhul v\u00f5ib kasutada t\u00e4iendavaid post hoc teste, et m\u00e4\u00e4rata kindlaks, millised konkreetsed r\u00fchmad erinevad \u00fcksteisest. Lisateavet post hoc testide kohta saate meie sisust \"<a href=\"https:\/\/mindthegraph.com\/blog\/post-hoc-analysis\/\" target=\"_blank\" rel=\"noreferrer noopener\">Post Hoc anal\u00fc\u00fcs: Protsess ja testide t\u00fc\u00fcbid<\/a>&#8220;.<\/p>\n\n\n\n<p>\u00dchepoolne ANOVA eeldab, et andmed on normaaljaotusega ja r\u00fchmade erinevused on v\u00f5rdsed. Kui need eeldused ei ole t\u00e4idetud, v\u00f5ib selle asemel kasutada alternatiivseid mitteparameetrilisi teste.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/researcher.life\/all-access-pricing?utm_source=mtg&amp;utm_campaign=all-access-promotion&amp;utm_medium=blog\"><img decoding=\"async\" loading=\"lazy\" width=\"1024\" height=\"410\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-1024x410.png\" alt=\"\" class=\"wp-image-55425\" srcset=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-1024x410.png 1024w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-300x120.png 300w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-768x307.png 768w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-1536x615.png 1536w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-2048x820.png 2048w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-18x7.png 18w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2024\/08\/Banner3-100x40.png 100w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n<h2 id=\"h-how-is-one-way-anova-used\"><strong>Kuidas kasutatakse \u00fchesuunalist ANOVA-d?<\/strong><\/h2>\n\n\n\n<p>\u00dchepoolne ANOVA on statistiline test, mida kasutatakse selleks, et teha kindlaks, kas kahe v\u00f5i enama s\u00f5ltumatu r\u00fchma keskmiste vahel on olulisi erinevusi. Seda kasutatakse nullh\u00fcpoteesi testimiseks, et k\u00f5ikide r\u00fchmade keskmised on v\u00f5rdsed, v\u00f5rreldes alternatiivse h\u00fcpoteesiga, et v\u00e4hemalt \u00fcks keskmine erineb teistest.<\/p>\n\n\n\n<h2 id=\"h-assumptions-of-anova\"><strong>ANOVA eeldused<\/strong><\/h2>\n\n\n\n<p>ANOVA-l on mitu eeldust, mis peavad olema t\u00e4idetud, et tulemused oleksid kehtivad ja usaldusv\u00e4\u00e4rsed. Need eeldused on j\u00e4rgmised:<\/p>\n\n\n\n<ul>\n<li><strong>Normaalsus:<\/strong> S\u00f5ltuv muutuja peaks olema igas r\u00fchmas normaalselt jaotunud. Seda saab kontrollida, kasutades histogramme, normaalse t\u00f5en\u00e4osuse graafikuid v\u00f5i statistilisi teste, nagu Shapiro-Wilki test.<\/li>\n\n\n\n<li><strong>Varieeruvuse homogeensus: <\/strong>S\u00f5ltuva muutuja dispersioon peaks olema k\u00f5igis r\u00fchmades ligikaudu v\u00f5rdne. Seda saab kontrollida selliste statistiliste testide abil nagu Levene'i test v\u00f5i Bartletti test.<\/li>\n\n\n\n<li><strong>S\u00f5ltumatus: <\/strong>Iga r\u00fchma vaatlused peaksid olema \u00fcksteisest s\u00f5ltumatud. See t\u00e4hendab, et \u00fche r\u00fchma v\u00e4\u00e4rtused ei tohiks olla seotud ega s\u00f5ltuda \u00fchegi teise r\u00fchma v\u00e4\u00e4rtustest.<\/li>\n\n\n\n<li><strong>Juhuslik valikuuring:<\/strong> R\u00fchmad tuleks moodustada juhusliku valimi v\u00f5tmise teel. See tagab, et tulemusi saab \u00fcldistada suuremale \u00fcldkogumile.<\/li>\n<\/ul>\n\n\n\n<p>Oluline on neid eeldusi enne ANOVA l\u00e4biviimist kontrollida, sest nende rikkumine v\u00f5ib viia ebat\u00e4psete tulemuste ja eba\u00f5igete j\u00e4reldusteni. Kui \u00fcks v\u00f5i mitu eeldust on rikutud, v\u00f5ib kasutada alternatiivseid teste, n\u00e4iteks mitteparameetrilisi teste.<\/p>\n\n\n\n<h2 id=\"h-performing-a-one-way-anova\"><strong>\u00dchepoolse ANOVA l\u00e4biviimine<\/strong><\/h2>\n\n\n\n<p>\u00dchepoolse ANOVA l\u00e4biviimiseks v\u00f5ite j\u00e4rgida j\u00e4rgmisi samme:<\/p>\n\n\n\n<p><strong>1. samm:<\/strong> Esitage h\u00fcpoteesid<\/p>\n\n\n\n<p>M\u00e4\u00e4ratlege nullh\u00fcpotees ja alternatiivh\u00fcpotees. Nullh\u00fcpotees on, et r\u00fchmade keskmiste vahel ei ole olulisi erinevusi. Alternatiivh\u00fcpotees on, et v\u00e4hemalt \u00fcks r\u00fchma keskmine erineb teistest oluliselt.<\/p>\n\n\n\n<p><strong>2. samm:<\/strong> Andmete kogumine<\/p>\n\n\n\n<p>Koguge andmeid igast r\u00fchmast, mida soovite v\u00f5rrelda. Iga r\u00fchm peaks olema s\u00f5ltumatu ja sarnase valimi suurusega.<\/p>\n\n\n\n<p><strong>3. samm:<\/strong> Arvutage iga r\u00fchma keskmine ja dispersioon.<\/p>\n\n\n\n<p>Arvutage iga r\u00fchma keskmine ja dispersioon, kasutades kogutud andmeid.<\/p>\n\n\n\n<p><strong>4. samm:<\/strong> Arvutage \u00fcldine keskmine ja dispersioon<\/p>\n\n\n\n<p>Arvutage \u00fcldine keskmine ja dispersioon, v\u00f5ttes iga r\u00fchma keskmiste ja dispersioonide keskmise.<\/p>\n\n\n\n<p><strong>5. samm:<\/strong> Arvutage r\u00fchmadevaheline ruutude summa (SSB).<\/p>\n\n\n\n<p>Arvutage r\u00fchmadevaheline ruutude summa (SSB), kasutades valemit:<\/p>\n\n\n\n<p>SSB = \u03a3ni (x\u0304i - x\u0304)^2<\/p>\n\n\n\n<p>kus ni on i-nda r\u00fchma valimi suurus, x\u0304i on i-nda r\u00fchma keskmine ja x\u0304 on \u00fcldine keskmine.<\/p>\n\n\n\n<p><strong>6. samm:<\/strong> Arvuta ruutude summa r\u00fchmades (SSW).<\/p>\n\n\n\n<p>Arvutage r\u00fchmade ruutude summa (SSW), kasutades valemit:<\/p>\n\n\n\n<p>SSW = \u03a3\u03a3(xi - x\u0304i)^2<\/p>\n\n\n\n<p>kus xi on i-nes vaatlus j-nes r\u00fchmas, x\u0304i on j-nes r\u00fchmas keskmine ja j on vahemikus 1 kuni k r\u00fchma.<\/p>\n\n\n\n<p><strong>7. samm: <\/strong>Arvutage F-statistik<\/p>\n\n\n\n<p>Arvutage F-statistik, jagades r\u00fchmadevahelise dispersiooni (SSB) r\u00fchmasisese dispersiooniga (SSW):<\/p>\n\n\n\n<p>F = (SSB \/ (k - 1)) \/ (SSW \/ (n - k))<\/p>\n\n\n\n<p>kus k on r\u00fchmade arv ja n on valimi kogumaht.<\/p>\n\n\n\n<p><strong>8. samm:<\/strong> M\u00e4\u00e4rake F-i kriitiline v\u00e4\u00e4rtus ja p-v\u00e4\u00e4rtus.<\/p>\n\n\n\n<p>M\u00e4\u00e4rake F kriitiline v\u00e4\u00e4rtus ja vastav p-v\u00e4\u00e4rtus soovitud olulisuse taseme ja vabadusastmete p\u00f5hjal.<\/p>\n\n\n\n<p><strong>9. samm:<\/strong> V\u00f5rrelda arvutatud F-statistikat kriitilise v\u00e4\u00e4rtusega F<\/p>\n\n\n\n<p>Kui arvutatud F-statistik on suurem kui F kriitiline v\u00e4\u00e4rtus, l\u00fckake nullh\u00fcpotees tagasi ja j\u00e4reldage, et v\u00e4hemalt kahe r\u00fchma keskmiste vahel on oluline erinevus. Kui arvutatud F-statistik on v\u00e4iksem v\u00f5i v\u00f5rdne F-i kriitilise v\u00e4\u00e4rtusega, ei l\u00fckata nullh\u00fcpoteesi tagasi ja j\u00e4reldatakse, et r\u00fchmade keskmiste vahel puudub oluline erinevus.<\/p>\n\n\n\n<p><strong>10. samm:<\/strong> post hoc anal\u00fc\u00fcs (vajaduse korral)<\/p>\n\n\n\n<p>Kui nullh\u00fcpotees l\u00fckatakse tagasi, tehke post hoc anal\u00fc\u00fcs, et m\u00e4\u00e4rata kindlaks, millised r\u00fchmad erinevad \u00fcksteisest oluliselt. Tavalised post hoc testid on Tukey HSD test, Bonferroni korrektsioon ja Scheffe test.<\/p>\n\n\n\n<h2 id=\"h-interpreting-the-results\"><strong>Tulemuste t\u00f5lgendamine<\/strong><\/h2>\n\n\n\n<p>P\u00e4rast \u00fchesuunalise ANOVA l\u00e4biviimist saab tulemusi t\u00f5lgendada j\u00e4rgmiselt:<\/p>\n\n\n\n<p><strong>F-statistika ja p-v\u00e4\u00e4rtus: <\/strong>F-statistik m\u00f5\u00f5dab r\u00fchmadevahelise ja r\u00fchmasisese dispersiooni suhet. P-v\u00e4\u00e4rtus n\u00e4itab, kui suur on t\u00f5en\u00e4osus, et F-statistik on sama \u00e4\u00e4rmuslik kui vaadeldav, kui nullh\u00fcpotees on t\u00f5ene. V\u00e4ike p-v\u00e4\u00e4rtus (v\u00e4iksem kui valitud olulisuse tase, tavaliselt 0,05) viitab tugevale t\u00f5endile nullh\u00fcpoteesi vastu, mis n\u00e4itab, et v\u00e4hemalt kahe r\u00fchma keskmiste vahel on oluline erinevus.<\/p>\n\n\n\n<p><strong>Vabadusastmed: <\/strong>R\u00fchmadevaheliste ja r\u00fchmasiseste tegurite vabadusastmed on vastavalt k-1 ja N-k, kus k on r\u00fchmade arv ja N on valimi kogumaht.<\/p>\n\n\n\n<p><strong>Keskmine ruutviga:<\/strong><em> <\/em>Keskmine ruutviga (MSE) on grupisisese ruutude summa ja grupisiseste vabadusastmete suhe. See kujutab endast iga r\u00fchma hinnangulist dispersiooni p\u00e4rast r\u00fchmadevaheliste erinevuste arvessev\u00f5tmist.<\/p>\n\n\n\n<p><strong>Efekti suurus:<\/strong> M\u00f5ju suurust saab m\u00f5\u00f5ta, kasutades eta-ruutu (\u03b7\u00b2), mis n\u00e4itab, kui suur osa s\u00f5ltuva muutuja koguvariatsioonist tuleneb r\u00fchmade erinevustest. Eta-kvoodi v\u00e4\u00e4rtuste tavalised t\u00f5lgendused on j\u00e4rgmised:<\/p>\n\n\n\n<p>V\u00e4ike m\u00f5ju: \u03b7\u00b2 &lt; 0,01<\/p>\n\n\n\n<p>Keskmine m\u00f5ju: 0,01 \u2264 \u03b7\u00b2 &lt; 0,06<\/p>\n\n\n\n<p>Suur m\u00f5ju: \u03b7\u00b2 \u2265 0,06<\/p>\n\n\n\n<p><a href=\"https:\/\/mindthegraph.com\/blog\/post-hoc-analysis\/\"><strong>Post hoc anal\u00fc\u00fcs:<\/strong><\/a> Kui nullh\u00fcpotees l\u00fckatakse tagasi, v\u00f5ib teha post hoc anal\u00fc\u00fcsi, et m\u00e4\u00e4rata kindlaks, millised r\u00fchmad erinevad \u00fcksteisest oluliselt. Selleks v\u00f5ib kasutada erinevaid teste, n\u00e4iteks Tukey HSD-testi, Bonferroni korrektsiooni v\u00f5i Scheffe testi.<\/p>\n\n\n\n<p>Tulemusi tuleb t\u00f5lgendada uurimisk\u00fcsimuse ja anal\u00fc\u00fcsi eelduste kontekstis. Kui eeldused ei ole t\u00e4idetud v\u00f5i kui tulemused ei ole t\u00f5lgendatavad, v\u00f5ib olla vaja alternatiivseid teste v\u00f5i muudatusi anal\u00fc\u00fcsis.<\/p>\n\n\n\n<h2 id=\"h-post-hoc-testing\"><strong>Post hoc testimine<\/strong><\/h2>\n\n\n\n<p>Statistikas on \u00fchesuunaline ANOVA tehnika, mida kasutatakse kolme v\u00f5i enama r\u00fchma keskmiste v\u00f5rdlemiseks. Kui ANOVA-test on tehtud ja kui nullh\u00fcpotees l\u00fckatakse tagasi, mis t\u00e4hendab, et on olulisi t\u00f5endeid selle kohta, et v\u00e4hemalt \u00fche r\u00fchma keskmine erineb teistest, v\u00f5ib teha post hoc testi, et teha kindlaks, millised r\u00fchmad erinevad \u00fcksteisest oluliselt.<\/p>\n\n\n\n<p>Post hoc teste kasutatakse selleks, et m\u00e4\u00e4rata kindlaks konkreetsed erinevused r\u00fchmade keskmiste vahel. M\u00f5ned tavalised post hoc testid h\u00f5lmavad Tukey ausalt olulist erinevust (HSD), Bonferroni korrektsiooni, Scheffe'i meetodit ja Dunnetti testi. Igal neist testidest on omad eeldused, eelised ja piirangud ning selle valik, millist testi kasutada, s\u00f5ltub konkreetsest uurimisk\u00fcsimusest ja andmete omadustest.<\/p>\n\n\n\n<p>\u00dcldiselt on post hoc testid kasulikud, et anda \u00fcksikasjalikumat teavet konkreetsete r\u00fchmade erinevuste kohta \u00fchesuunalises ANOVA anal\u00fc\u00fcsis. Siiski on oluline kasutada neid teste ettevaatlikult ja t\u00f5lgendada tulemusi uurimisk\u00fcsimuse ja andmete erip\u00e4ra kontekstis.<\/p>\n\n\n\n<p>Lisateave Post Hoc anal\u00fc\u00fcsi kohta meie sisust \"<a href=\"https:\/\/mindthegraph.com\/blog\/post-hoc-analysis\/\">Post Hoc anal\u00fc\u00fcs: Protsess ja testide t\u00fc\u00fcbid<\/a>&#8220;.<\/p>\n\n\n\n<h2 id=\"h-reporting-the-results-of-anova\"><strong>ANOVA tulemuste esitamine<\/strong><\/h2>\n\n\n\n<p>ANOVA-anal\u00fc\u00fcsi tulemuste esitamisel tuleb esitada mitu teavet:<\/p>\n\n\n\n<p><strong>F-statistika: <\/strong>See on ANOVA teststatistik ja kujutab endast r\u00fchmadevahelise dispersiooni ja r\u00fchmasisese dispersiooni suhet.<\/p>\n\n\n\n<p><strong>F-statistika vabadusastmed:<\/strong> See h\u00f5lmab vabadusastmeid lugeja (r\u00fchmadevaheline varieerumine) ja nimetaja (r\u00fchmasisese varieerumise) jaoks.<\/p>\n\n\n\n<p><strong>P-v\u00e4\u00e4rtus: <\/strong>See n\u00e4itab t\u00f5en\u00e4osust, et vaadeldav F-statistik (v\u00f5i \u00e4\u00e4rmuslikum v\u00e4\u00e4rtus) saadakse ainult juhuslikult, eeldades, et nullh\u00fcpotees on t\u00f5ene.<\/p>\n\n\n\n<p><strong>V\u00e4ide selle kohta, kas nullh\u00fcpotees l\u00fckati tagasi v\u00f5i mitte:<\/strong> See peaks p\u00f5hinema p-v\u00e4\u00e4rtusel ja valitud olulisuse tasemel (nt alfa = 0,05).<\/p>\n\n\n\n<p><strong>Post hoc testimine:<\/strong> Kui nullh\u00fcpotees l\u00fckatakse tagasi, siis tuleb esitada post hoc testimise tulemused, et teha kindlaks, millised r\u00fchmad erinevad \u00fcksteisest oluliselt.<\/p>\n\n\n\n<p>N\u00e4iteks v\u00f5ib n\u00e4idisaruanne olla j\u00e4rgmine:<\/p>\n\n\n\n<p>Kolme r\u00fchma (r\u00fchm A, r\u00fchm B ja r\u00fchm C) keskmiste tulemuste v\u00f5rdlemiseks m\u00e4lu s\u00e4ilitamise testis viidi l\u00e4bi \u00fchesuunaline ANOVA. F-statistikaks oli 4,58, vabadusastmete arv 2, 87 ja p-v\u00e4\u00e4rtus 0,01. Nullh\u00fcpotees l\u00fckati tagasi, mis n\u00e4itab, et m\u00e4lu s\u00e4ilitamise skoorides oli v\u00e4hemalt \u00fches r\u00fchmas oluline erinevus. post hoc testimine, kasutades Tukey HSD, n\u00e4itas, et r\u00fchma A (M = 83,4, SD = 4,2) keskmine tulemus oli oluliselt k\u00f5rgem kui nii r\u00fchma B (M = 76,9, SD = 5,5) kui ka r\u00fchma C (M = 77,6, SD = 5,3), mis ei erinenud \u00fcksteisest oluliselt.<\/p>\n\n\n\n<h2 id=\"h-find-the-perfect-infographic-template-for-you\"><strong>Leia endale sobiv infograafia mall<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/mindthegraph.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Mind the Graph<\/a> on platvorm, mis pakub ulatuslikku kollektsiooni eelnevalt kujundatud infograafiamalle, et aidata teadlastel ja uurijatel luua visuaalseid abivahendeid, mis edastavad t\u00f5husalt teaduslikke kontseptsioone. Platvorm pakub juurdep\u00e4\u00e4su suurele teaduslike illustratsioonide raamatukogule, mis tagab, et teadlased ja uurijad leiavad h\u00f5lpsasti t\u00e4iusliku infograafiamalli oma uurimistulemuste visuaalseks edastamiseks.<\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><a href=\"https:\/\/mindthegraph.com\/offer-trial\"><img decoding=\"async\" loading=\"lazy\" width=\"651\" height=\"174\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2023\/02\/banner-blog-trial-04.jpg\" alt=\"\" class=\"wp-image-26792\" srcset=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2023\/02\/banner-blog-trial-04.jpg 651w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2023\/02\/banner-blog-trial-04-300x80.jpg 300w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2023\/02\/banner-blog-trial-04-18x5.jpg 18w, https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2023\/02\/banner-blog-trial-04-100x27.jpg 100w\" sizes=\"(max-width: 651px) 100vw, 651px\" \/><\/a><\/figure><\/div>\n\n\n<div style=\"height:44px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>","protected":false},"excerpt":{"rendered":"<p>\u00d5ppige tundma \u00fchesuunalist ANOVA-d, statistilist meetodit, mida kasutatakse mitme r\u00fchma keskmiste v\u00f5rdlemiseks andmeanal\u00fc\u00fcsis, ja kuidas seda rakendada.<\/p>","protected":false},"author":35,"featured_media":29180,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[959,28],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.9 - 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