{"id":28434,"date":"2023-06-20T18:17:37","date_gmt":"2023-06-20T21:17:37","guid":{"rendered":"https:\/\/mindthegraph.com\/blog\/psychedelic-medicine-copy\/"},"modified":"2023-07-03T18:36:10","modified_gmt":"2023-07-03T21:36:10","slug":"regression-analysis","status":"publish","type":"post","link":"https:\/\/mindthegraph.com\/blog\/et\/regressioonianaluus\/","title":{"rendered":"Regressioonanal\u00fc\u00fcsi kasutamine keeruliste seoste m\u00f5istmiseks"},"content":{"rendered":"<p>Regressioonanal\u00fc\u00fcs on l\u00e4henemisviis \u00fche v\u00f5i mitme s\u00f5ltumatu muutuja ja s\u00f5ltuva muutuja vahelise seose kindlakstegemiseks ja anal\u00fc\u00fcsimiseks. Seda meetodit kasutatakse laialdaselt mitmetes valdkondades, sealhulgas tervishoius, sotsiaalteadustes, inseneriteadustes, majanduses ja \u00e4ris. Regressioonanal\u00fc\u00fcsi abil saate uurida andmete p\u00f5hilisi seoseid ja t\u00f6\u00f6tada v\u00e4lja prognoosimudelid, mis aitavad teil teha teadlikke otsuseid.<\/p>\n\n\n\n<p>See artikkel annab teile p\u00f5hjaliku \u00fclevaate regressioonanal\u00fc\u00fcsist, sealhulgas selle toimimisest, lihtsasti m\u00f5istetava n\u00e4ite ja selgitab, kuidas see erineb korrelatsioonianal\u00fc\u00fcsist.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-regression-analysis\">Mis on regressioonanal\u00fc\u00fcs?<\/h2>\n\n\n\n<p>Regressioonanal\u00fc\u00fcs on statistiline meetod s\u00f5ltuva muutuja ja \u00fche v\u00f5i mitme s\u00f5ltumatu muutuja vahelise seose kindlakstegemiseks ja kvantifitseerimiseks. L\u00fchidalt \u00f6eldes aitab see m\u00f5ista, kuidas \u00fche v\u00f5i mitme s\u00f5ltumatu muutuja muutused on seotud s\u00f5ltuva muutuja muutustega.<\/p>\n\n\n\n<p>Regressioonanal\u00fc\u00fcsist p\u00f5hjaliku arusaamise saamiseks peate k\u00f5igepealt m\u00f5istma j\u00e4rgmisi m\u00f5isteid:<\/p>\n\n\n\n<ul>\n<li><strong>S\u00f5ltuv muutuja: <\/strong>See on muutuja, mille anal\u00fc\u00fcsimisest v\u00f5i prognoosimisest te olete huvitatud. See on tulemusmuutuja, mida p\u00fc\u00fcate m\u00f5ista ja selgitada.<\/li>\n\n\n\n<li><strong>S\u00f5ltumatud muutujad: <\/strong>Need on muutujad, mis teie arvates m\u00f5jutavad s\u00f5ltuvat muutujat. Neid nimetatakse sageli prognoosimuutujateks, kuna neid kasutatakse s\u00f5ltuva muutuja muutuste prognoosimiseks v\u00f5i selgitamiseks.<\/li>\n<\/ul>\n\n\n\n<p>Regressioonanal\u00fc\u00fcsi saab kasutada mitmesugustel juhtudel, sealhulgas s\u00f5ltuva muutuja tulevaste v\u00e4\u00e4rtuste prognoosimiseks, s\u00f5ltumatute muutujate m\u00f5ju m\u00f5istmiseks s\u00f5ltuvale muutujale ning k\u00f5rvalekallete v\u00f5i ebatavaliste juhtumite leidmiseks andmete kogumisel.<\/p>\n\n\n\n<p>Regressioonanal\u00fc\u00fcsi v\u00f5ib liigitada mitmeks t\u00fc\u00fcbiks, sealhulgas \u00fchekordne lineaarne regressioon, logistiline regressioon, pol\u00fcnoomne regressioon ja mitmekordne regressioon. Sobiv regressioonimudel m\u00e4\u00e4ratakse kindlaks andmete iseloomu ja uuritava teema j\u00e4rgi.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-how-does-regression-analysis-work\">Kuidas toimib regressioonanal\u00fc\u00fcs?<\/h2>\n\n\n\n<p>Regressioonanal\u00fc\u00fcsi eesm\u00e4rk on leida k\u00f5ige paremini sobiv joon v\u00f5i k\u00f5ver, mis kajastab seost s\u00f5ltumatute muutujate ja s\u00f5ltuva muutuja vahel. See k\u00f5ige paremini sobiv joon v\u00f5i k\u00f5ver genereeritakse statistiliste meetodite abil, mis v\u00e4hendavad erinevusi andmekogumi eeldatavate ja tegelike v\u00e4\u00e4rtuste vahel.<\/p>\n\n\n\n<p>Siin on esitatud kahe k\u00f5ige levinuma regressioonianal\u00fc\u00fcsi t\u00fc\u00fcbi valemid:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-single-linear-regression\">\u00dchtne lineaarne regressioon<\/h3>\n\n\n\n<p>Lihtsa lineaarse regressiooni puhul kasutate kahe muutuja - s\u00f5ltumatu muutuja (x) ja s\u00f5ltuva muutuja (y) - vahelise seose n\u00e4itamiseks parima sobivuse joont.<\/p>\n\n\n\n<p>Parima sobivuse joont v\u00f5ib esitada v\u00f5rrandiga: y = a + bx.<\/p>\n\n\n\n<p>Siin on a l\u00f5ikepunkt, b on joone kalle. Kalde arvutamiseks kasutatakse valemit: b = (n\u03a3(xy) - \u03a3x\u03a3y) \/ (n\u03a3(x<sup>2<\/sup>) - (\u03a3x)<sup>2<\/sup>), kus n on vaatluste arv, \u03a3xy on x ja y summa, \u03a3x ja \u03a3y on vastavalt x ja y summad ja \u03a3(x<sup>2<\/sup>) on x ruutude summa.<\/p>\n\n\n\n<p>L\u00f5ikepunkti arvutamiseks kasutatakse valemit: a = (\u03a3y - b\u03a3x) \/ n.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-multiple-regression\">Mitmekordne regressioon&nbsp;<\/h3>\n\n\n\n<p>Mitmekordne lineaarne regressioon:<\/p>\n\n\n\n<p>Mitme lineaarse regressioonimudeli v\u00f5rrandi valem on:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p><strong>y = b<sub>0<\/sub> + b<sub>1<\/sub>x<sub>1<\/sub> + b<sub>2<\/sub>x<sub>2<\/sub> + ... + b<sub>n<\/sub>x<sub>n<\/sub><\/strong><\/p>\n<\/blockquote>\n\n\n\n<p>kus y on s\u00f5ltuv muutuja, x<sub>1<\/sub>, x<sub>2<\/sub>, ..., x<sub>n<\/sub> on s\u00f5ltumatud muutujad ja b<sub>0<\/sub>, b<sub>1<\/sub>, b<sub>2<\/sub>, ..., bn on s\u00f5ltumatute muutujate koefitsiendid.<\/p>\n\n\n\n<p>Koefitsientide hindamise valem tavap\u00e4raste v\u00e4himate ruutude abil on j\u00e4rgmine:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote\">\n<p><strong>\u03b2 = (X'X)<sup>(-1)<\/sup>X'y<\/strong><\/p>\n<\/blockquote>\n\n\n\n<p>kus \u03b2 on koefitsientide veeruvektor, X on s\u00f5ltumatute muutujate disainimaatriks, X' on X transponeeritud ja y on s\u00f5ltuva muutuja vaatluste vektor.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-regression-analysis-example\">Regressioonanal\u00fc\u00fcsi n\u00e4ide<\/h2>\n\n\n\n<p>Oletame, et soovite uurida seost inimese keskmise hinde (GPA) ja n\u00e4dalas \u00f5pitud tundide arvu vahel. Te kogute teavet mitmete \u00fcli\u00f5pilaste kohta, sealhulgas nende \u00f5ppetundide arvu ja keskmise hinde kohta.<\/p>\n\n\n\n<p>Seej\u00e4rel kasutage regressioonianal\u00fc\u00fcsi, et n\u00e4ha, kas m\u00f5lema muutuja vahel on lineaarne seos, ja kui see on nii, saate koostada mudeli, mis ennustab \u00f5pilase GPA-d nende n\u00e4dalas \u00f5pitud tundide arvu p\u00f5hjal.<\/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 is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/lh5.googleusercontent.com\/jY2vs2UsuRYMfVS7ZwPuk_epkVR-Yl7jnG8al1mDmUs6L8YsZ_X3WwNFy40jDCareFFtyOzL6b_DXIhO8FrJR1CMyVwg_rHyE1jycXX-LGWLsUf4LTzWV4L35ObUSidK1EsF136nqG-tHj_zjStgbbA\" alt=\"\" width=\"505\" height=\"263\"\/><figcaption class=\"wp-element-caption\"><em>Pilt saadaval aadressil <a href=\"https:\/\/www.alchemer.com\" target=\"_blank\" rel=\"noreferrer noopener\">alchemer.com<\/a><\/em><\/figcaption><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/www.alchemer.com\/wp-content\/uploads\/2019\/04\/regression-analysis-1.png\"><\/a><\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Kui andmed joonistada hajuvuskaardile, ilmneb, et \u00f5ppetundide ja GPA vahel on soodne lineaarne seos. Seej\u00e4rel hinnatakse lihtsa lineaarse regressioonimudeli abil parima sobivuse joone t\u00f5usu ja l\u00f5ikepunkti. L\u00f5plik lahendus v\u00f5iks v\u00e4lja n\u00e4ha selline:<\/p>\n\n\n\n<p>GPA = 2,0 + 0,3 (\u00f5ppetunnid n\u00e4dalas)<\/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 is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/lh6.googleusercontent.com\/plMkcFRz9dE-xiHm7wkzhCBplbaGIBdvzy4y8LmGqBEaFAMV72IUx7DRx8uvaU_TVMkcOlwcgH_s12NMZFjni4gWrlANjcBH2RqyoFKzrks9q3SGUDpnd_ILZZ4ookIPxD-PJ2T5L-HS3GaWCJf8yEE\" alt=\"\" width=\"505\" height=\"263\"\/><figcaption class=\"wp-element-caption\"><em><em>Pilt saadaval aadressil <a href=\"https:\/\/www.alchemer.com\" target=\"_blank\" rel=\"noreferrer noopener\">alchemer.com<\/a><\/em><\/em><\/figcaption><\/figure><\/div>\n\n\n<p><a href=\"https:\/\/www.alchemer.com\/wp-content\/uploads\/2019\/04\/regression-analysis-2.png\"><\/a><\/p>\n\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>See v\u00f5rrand \u00fctleb, et iga lisatunni eest n\u00e4dalas t\u00f5useb \u00f5pilase keskmine hinne 0,3 punkti v\u00f5rra, kusjuures k\u00f5ik muu on samav\u00e4\u00e4rne. Seda algoritmi saab kasutada \u00f5pilase GPA prognoosimiseks selle p\u00f5hjal, mitu tundi ta n\u00e4dalas \u00f5pib, ning samuti selleks, et teha kindlaks, millistel \u00f5pilastel on nende \u00f5pirutiini p\u00f5hjal oht, et nad j\u00e4\u00e4vad alla.&nbsp;<\/p>\n\n\n\n<p>Kasutades n\u00e4ites esitatud andmeid, on v\u00e4\u00e4rtused jaoks <strong>b<\/strong> ja <strong>a<\/strong> on j\u00e4rgmised:<\/p>\n\n\n\n<p>n = 10 (vaatluste arv)<\/p>\n\n\n\n<p>\u03a3x = 30 (\u00f5ppetundide summa)<\/p>\n\n\n\n<p>\u03a3y = 25 (GPAde summa)<\/p>\n\n\n\n<p>\u03a3xy = 149 (\u00f5ppetundide ja GPAde summa)<\/p>\n\n\n\n<p>\u03a3(x)<sup>2<\/sup> = 102 (\u00f5ppetundide ruutude summa)<\/p>\n\n\n\n<p>Kasutades neid v\u00e4\u00e4rtusi, arvutage <strong>b<\/strong> nagu:<\/p>\n\n\n\n<p>b = (n\u03a3(xy) - \u03a3x\u03a3y) \/ (n\u03a3(x<sup>2<\/sup>) - (\u03a3x)<sup>2<\/sup>)<\/p>\n\n\n\n<p>= (10 * 149 &#8211; 30 * 25) \/ (10 * 102 &#8211; 30<sup>2<\/sup>)<\/p>\n\n\n\n<p>= 0.3<\/p>\n\n\n\n<p>Ja arvutada <strong>a <\/strong>nagu:<\/p>\n\n\n\n<p>a = (\u03a3y - b\u03a3x) \/ n<\/p>\n\n\n\n<p>= (25 &#8211; 0.3 * 30) \/ 10<\/p>\n\n\n\n<p>= 2.0<\/p>\n\n\n\n<p>Seega on parima sobivuse joone v\u00f5rrand:&nbsp;<\/p>\n\n\n\n<p>GPA = 2,0 + 0,3 (\u00f5ppetunnid n\u00e4dalas)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-the-difference-between-correlation-and-regression\">Mis vahe on korrelatsioonil ja regressioonil?<\/h2>\n\n\n\n<p>Nii korrelatsioon kui ka regressioon on statistilised meetodid kahe muutuja vahelise seose uurimiseks. Nad teenivad erinevaid eesm\u00e4rke ja annavad erinevat liiki teavet.<\/p>\n\n\n\n<p>Korrelatsioon on kahe muutuja vahelise seose tugevuse ja kulgemise m\u00f5\u00f5t. See ulatub -1-st kuni +1-ni, kusjuures -1 t\u00e4histab t\u00e4iuslikku negatiivset korrelatsiooni, 0 t\u00e4histab korrelatsiooni puudumist ja +1 t\u00e4histab t\u00e4iuslikku positiivset korrelatsiooni. Korrelatsioon n\u00e4itab, mil m\u00e4\u00e4ral on kaks muutujat omavahel seotud, kuid see ei n\u00e4ita p\u00f5hjust ega ennustatavust.<\/p>\n\n\n\n<p>Regressioon on seevastu meetod kahe muutuja vahelise seose modelleerimiseks, tavaliselt selleks, et prognoosida v\u00f5i selgitada \u00fchte muutujat teise muutuja p\u00f5hjal. Regressioonanal\u00fc\u00fcsiga saab anda hinnanguid seose suuruse ja suuna kohta, samuti statistilise olulisuse teste, usaldusvahemikke ja tulevaste tulemuste prognoose.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-your-creations-ready-within-minutes\">Teie looming, valmis m\u00f5ne minutiga<\/h2>\n\n\n\n<p><a href=\"https:\/\/mindthegraph.com\" target=\"_blank\" rel=\"noreferrer noopener\">Mind the Graph<\/a> on veebiplatvorm, mis pakub teile ulatuslikku raamatukogu teaduslikke illustratsioone ja infograafilisi kujundusi, mida saab lihtsalt muuta vastavalt teie unikaalsetele vajadustele. Valmistage professionaalse v\u00e4limusega graafikuid, plakateid ja graafilisi kokkuv\u00f5tteid minutitega, kasutades drag-and-drop-liidest ning mitmesuguseid t\u00f6\u00f6riistu ja funktsioone.&nbsp;<\/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\"><img decoding=\"async\" loading=\"lazy\" width=\"800\" height=\"500\" src=\"https:\/\/mindthegraph.com\/blog\/wp-content\/uploads\/2023\/05\/banco.gif\" alt=\"\" class=\"wp-image-28087\"\/><\/figure><\/div>\n\n\n<div style=\"height:21px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"is-layout-flex wp-block-buttons\">\n<div class=\"wp-block-button aligncenter\"><a class=\"wp-block-button__link has-background wp-element-button\" href=\"https:\/\/mindthegraph.com\/\" style=\"border-radius:50px;background-color:#dc1866\" target=\"_blank\" rel=\"noreferrer noopener\">Alustage loomist Mind the Graph-ga<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:44px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>","protected":false},"excerpt":{"rendered":"<p>M\u00f5ista, kuidas regressioonianal\u00fc\u00fcs t\u00f6\u00f6tab h\u00f5lpsasti, kasutades p\u00f5hjalikku n\u00e4idet, ja \u00f5ppida k\u00f5ige levinumad valemid. <\/p>","protected":false},"author":28,"featured_media":28437,"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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