{"id":1096,"date":"2023-07-27T16:38:31","date_gmt":"2023-07-27T16:38:31","guid":{"rendered":"https:\/\/statorials.org\/tr\/lojistik-regresyon-hipotezleri\/"},"modified":"2023-07-27T16:38:31","modified_gmt":"2023-07-27T16:38:31","slug":"lojistik-regresyon-hipotezleri","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/lojistik-regresyon-hipotezleri\/","title":{"rendered":"Lojistik regresyonun 6 hipotezi (\u00f6rneklerle)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Lojistik regresyon,<\/strong> <a style=\"color: #000000;\" href=\"https:\/\/statorials.org\/tr\/degiskenleri-aciklayici-yanitlar\/\" target=\"_blank\" rel=\"noopener noreferrer\">yan\u0131t de\u011fi\u015fkeni<\/a> ikili oldu\u011funda bir regresyon modeline uymak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Lojistik regresyon, bir modeli bir veri k\u00fcmesine yerle\u015ftirmeden \u00f6nce a\u015fa\u011f\u0131daki varsay\u0131mlar\u0131 yapar:<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Varsay\u0131m #1: Yan\u0131t de\u011fi\u015fkeni ikilidir<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Lojistik regresyon, yan\u0131t de\u011fi\u015fkeninin yaln\u0131zca iki olas\u0131 sonucu oldu\u011funu varsayar. \u0130\u015fte baz\u0131 \u00f6rnekler:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Evet veya hay\u0131r<\/span><\/li>\n<li> <span style=\"color: #000000;\">Erkek veya kad\u0131n<\/span><\/li>\n<li> <span style=\"color: #000000;\">Ba\u015far\u0131l\u0131 veya ba\u015far\u0131s\u0131z<\/span><\/li>\n<li> <span style=\"color: #000000;\">Yaz\u0131l\u0131 veya yaz\u0131s\u0131z<\/span><\/li>\n<li> <span style=\"color: #000000;\">K\u00f6t\u00fc huylu veya iyi huylu<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>Bu varsay\u0131m nas\u0131l kontrol edilir:<\/strong> Yan\u0131t de\u011fi\u015fkeninde meydana gelen benzersiz sonu\u00e7lar\u0131n say\u0131s\u0131n\u0131 basit\u00e7e say\u0131n. \u0130kiden fazla olas\u0131 sonu\u00e7 varsa bunun yerine <a href=\"https:\/\/en.wikipedia.org\/wiki\/Ordinal_regression\" target=\"_blank\" rel=\"noopener noreferrer\">s\u0131ral\u0131 regresyon<\/a> ger\u00e7ekle\u015ftirmeniz gerekecektir.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Hipotez #2: g\u00f6zlemler ba\u011f\u0131ms\u0131zd\u0131r<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Lojistik regresyon, veri k\u00fcmesindeki g\u00f6zlemlerin birbirinden ba\u011f\u0131ms\u0131z oldu\u011funu varsayar. Yani g\u00f6zlemler ayn\u0131 bireyin tekrarlanan \u00f6l\u00e7\u00fcmlerinden gelmemeli veya hi\u00e7bir \u015fekilde birbiriyle ili\u015fkili olmamal\u0131d\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Bu hipotez nas\u0131l test edilir:<\/strong> Bu hipotezi test etmenin en basit yolu, zamana kar\u015f\u0131 art\u0131klar\u0131n grafi\u011fini (yani g\u00f6zlem s\u0131ras\u0131) olu\u015fturmak ve rastgele bir e\u011filim olup olmad\u0131\u011f\u0131n\u0131 g\u00f6zlemlemektir. E\u011fer rastgele bir model <em>yoksa<\/em> bu varsay\u0131m ihlal edilebilir.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Hipotez #3: A\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler aras\u0131nda \u00e7oklu ba\u011flant\u0131 yoktur<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Lojistik regresyon, <a href=\"https:\/\/statorials.org\/tr\/degiskenleri-aciklayici-yanitlar\/\" target=\"_blank\" rel=\"noopener noreferrer\">a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler<\/a> aras\u0131nda ciddi bir <a href=\"https:\/\/statorials.org\/tr\/coklu-baglanti-regresyonu\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u00e7oklu ba\u011flant\u0131n\u0131n<\/a> olmad\u0131\u011f\u0131n\u0131 varsayar.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u00c7oklu do\u011frusall\u0131k, iki veya daha fazla a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenin regresyon modelinde benzersiz veya ba\u011f\u0131ms\u0131z bilgi sa\u011flamayacak \u015fekilde birbiriyle y\u00fcksek d\u00fczeyde korelasyona sahip olmas\u0131 durumunda ortaya \u00e7\u0131kar. De\u011fi\u015fkenler aras\u0131ndaki korelasyon derecesi yeterince y\u00fcksekse, bu durum modelin yerle\u015ftirilmesinde ve yorumlanmas\u0131nda sorunlara neden olabilir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u00d6rne\u011fin, yan\u0131t de\u011fi\u015fkeni olarak <strong>maksimum dikey s\u0131\u00e7ramay\u0131<\/strong> ve a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler olarak a\u015fa\u011f\u0131daki de\u011fi\u015fkenleri kullanarak bir lojistik regresyon ger\u00e7ekle\u015ftirmek istedi\u011finizi varsayal\u0131m:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Oyuncu boyutu<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Oyuncu boyutu<\/strong><\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>G\u00fcnde pratik yapmak i\u00e7in harcanan saatler<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Bu durumda, uzun boylu insanlar daha b\u00fcy\u00fck ayakkab\u0131 numaralar\u0131na sahip olma e\u011filiminde oldu\u011fundan, <strong>boy<\/strong> ve <strong>ayakkab\u0131 numaras\u0131<\/strong> muhtemelen y\u00fcksek oranda ili\u015fkilidir. Bu, regresyonda bu iki de\u011fi\u015fkeni kullan\u0131rsak \u00e7oklu do\u011frusall\u0131\u011f\u0131n muhtemelen bir sorun olaca\u011f\u0131 anlam\u0131na gelir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Bu varsay\u0131m nas\u0131l kontrol edilir:<\/strong> \u00c7oklu do\u011frusall\u0131\u011f\u0131 tespit etmenin en yayg\u0131n yolu, bir regresyon modelinde \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenler aras\u0131ndaki korelasyonu ve korelasyonun g\u00fcc\u00fcn\u00fc \u00f6l\u00e7en varyans enflasyon fakt\u00f6r\u00fcn\u00fc (VIF) kullanmakt\u0131r. VIF de\u011ferlerinin nas\u0131l hesaplanaca\u011f\u0131 ve yorumlanaca\u011f\u0131na ili\u015fkin ayr\u0131nt\u0131l\u0131 bir a\u00e7\u0131klama i\u00e7in <a href=\"https:\/\/statorials.org\/tr\/coklu-baglanti-regresyonu\/\" target=\"_blank\" rel=\"noopener noreferrer\">bu e\u011fitime<\/a> g\u00f6z at\u0131n.<\/span><\/p>\n<h3> <strong>Varsay\u0131m #4: A\u015f\u0131r\u0131 ayk\u0131r\u0131 de\u011ferler yoktur<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Lojistik regresyon, veri setinde a\u015f\u0131r\u0131 ayk\u0131r\u0131 de\u011ferlerin veya etkili g\u00f6zlemlerin olmad\u0131\u011f\u0131n\u0131 varsayar.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Bu varsay\u0131m nas\u0131l kontrol edilir:<\/strong> Bir veri k\u00fcmesindeki a\u015f\u0131r\u0131 ayk\u0131r\u0131 de\u011ferleri ve etkili g\u00f6zlemleri test etmenin en yayg\u0131n yolu, her g\u00f6zlem i\u00e7in <a href=\"https:\/\/statorials.org\/tr\/asci-mesafesini-kullanarak-etkili-veri-noktalarinin-nasil-belirlenecegi\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cook mesafesini<\/a> hesaplamakt\u0131r. Ger\u00e7ekten ayk\u0131r\u0131 de\u011ferler varsa, (1) bunlar\u0131 kald\u0131rmay\u0131, (2) bunlar\u0131 ortalama veya medyan gibi bir de\u011ferle de\u011fi\u015ftirmeyi veya (3) bunlar\u0131 yaln\u0131zca modelde tutmay\u0131 ancak regresyonu rapor ederken not etmeyi se\u00e7ebilirsiniz. . sonu\u00e7lar.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Hipotez #5: A\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler ile yan\u0131t de\u011fi\u015fkeninin logiti aras\u0131nda do\u011frusal bir ili\u015fki vard\u0131r<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Lojistik regresyon, her a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken ile yan\u0131t de\u011fi\u015fkeninin logiti aras\u0131nda do\u011frusal bir ili\u015fki oldu\u011funu varsayar. Logit&#8217;in \u015fu \u015fekilde tan\u0131mland\u0131\u011f\u0131n\u0131 hat\u0131rlay\u0131n:<\/span><\/p>\n<p> <span style=\"color: #000000;\">Logit(p) = log(p \/ (1-p)) burada p pozitif bir sonucun olas\u0131l\u0131\u011f\u0131d\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Bu hipotez nas\u0131l test edilir:<\/strong> Bu hipotezin do\u011fru olup olmad\u0131\u011f\u0131n\u0131 g\u00f6rmenin en kolay yolu Box-Tidwell testi kullanmakt\u0131r.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Varsay\u0131m #6: \u00d6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fc yeterince b\u00fcy\u00fck<\/strong><\/span><\/h3>\n<p> Lojistik regresyon, veri k\u00fcmesinin \u00f6rneklem boyutunun, uygun lojistik regresyon modelinden ge\u00e7erli sonu\u00e7lar \u00e7\u0131karmaya yetecek kadar b\u00fcy\u00fck oldu\u011funu varsayar.<\/p>\n<p> <span style=\"color: #000000;\"><strong>Bu hipotez nas\u0131l kontrol edilir:<\/strong> Genel bir kural olarak, her a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken i\u00e7in en az s\u0131kl\u0131kta sonuca sahip en az 10 vakan\u0131z olmal\u0131d\u0131r. \u00d6rne\u011fin, 3 a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkeniniz varsa ve en az s\u0131kl\u0131kta olan sonucun beklenen olas\u0131l\u0131\u011f\u0131 0,20 ise, o zaman \u00f6rneklem boyutunuz en az (10*3) \/ 0,20 = <strong>150<\/strong> olmal\u0131d\u0131r.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Lojistik Regresyonun Varsay\u0131mlar\u0131 vs. Do\u011frusal Regresyon<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Do\u011frusal regresyonun aksine lojistik regresyon \u015funlar\u0131 gerektirmez:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">A\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken(ler) ile yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki do\u011frusal ili\u015fki.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Modelin art\u0131klar\u0131 normal olarak da\u011f\u0131t\u0131lacakt\u0131r.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Art\u0131klar\u0131n sabit varyans\u0131 olmas\u0131 gerekir; bu ayn\u0131 zamanda <a href=\"https:\/\/statorials.org\/tr\/degisen-varyans-regresyonu\/\" target=\"_blank\" rel=\"noopener noreferrer\">e\u015fvaryans<\/a> olarak da bilinir.<\/span><\/li>\n<\/ul>\n<p> <strong><span style=\"color: #000000;\">\u0130lgili:<\/span><\/strong> <a href=\"https:\/\/statorials.org\/tr\/dogrusal-regresyon-varsayimlari\/\" target=\"_blank\" rel=\"noopener noreferrer\">Do\u011frusal Regresyonun D\u00f6rt Varsay\u0131m\u0131<\/a><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ek kaynaklar<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/tr\/lojistik-regresyon-gercek-ornekleri\/\" target=\"_blank\" rel=\"noopener noreferrer\">Lojistik Regresyonun Ger\u00e7ek Hayatta Kullan\u0131m\u0131na \u0130li\u015fkin 4 \u00d6rnek<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/spss-lojistik-regresyon\/\" target=\"_blank\" rel=\"noopener noreferrer\">SPSS&#8217;de lojistik regresyon nas\u0131l yap\u0131l\u0131r<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/lojistik-regresyon-excel\/\" target=\"_blank\" rel=\"noopener noreferrer\">Excel&#8217;de Lojistik Regresyon Nas\u0131l Ger\u00e7ekle\u015ftirilir<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/lojistik-regresyon-istatistikleri\/\" target=\"_blank\" rel=\"noopener noreferrer\">Stata&#8217;da lojistik regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Lojistik regresyon, yan\u0131t de\u011fi\u015fkeni ikili oldu\u011funda bir regresyon modeline uymak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir. Lojistik regresyon, bir modeli bir veri k\u00fcmesine yerle\u015ftirmeden \u00f6nce a\u015fa\u011f\u0131daki varsay\u0131mlar\u0131 yapar: Varsay\u0131m #1: Yan\u0131t de\u011fi\u015fkeni ikilidir Lojistik regresyon, yan\u0131t de\u011fi\u015fkeninin yaln\u0131zca iki olas\u0131 sonucu oldu\u011funu varsayar. \u0130\u015fte baz\u0131 \u00f6rnekler: Evet veya hay\u0131r Erkek veya kad\u0131n Ba\u015far\u0131l\u0131 veya ba\u015far\u0131s\u0131z Yaz\u0131l\u0131 veya [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1096","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Lojistik regresyonun 6 hipotezi (\u00f6rneklerle)<\/title>\n<meta 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