{"id":551,"date":"2023-07-29T13:07:26","date_gmt":"2023-07-29T13:07:26","guid":{"rendered":"https:\/\/statorials.org\/tr\/dogrusal-regresyon-varsayimlari\/"},"modified":"2023-07-29T13:07:26","modified_gmt":"2023-07-29T13:07:26","slug":"dogrusal-regresyon-varsayimlari","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/dogrusal-regresyon-varsayimlari\/","title":{"rendered":"Do\u011frusal regresyonun d\u00f6rt varsay\u0131m\u0131"},"content":{"rendered":"

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Do\u011frusal regresyon<\/strong> , iki de\u011fi\u015fken (x ve y) aras\u0131ndaki ili\u015fkiyi anlamak i\u00e7in kullanabilece\u011fimiz yararl\u0131 bir istatistiksel y\u00f6ntemdir. Ancak do\u011frusal regresyon ger\u00e7ekle\u015ftirmeden \u00f6nce d\u00f6rt varsay\u0131m\u0131n kar\u015f\u0131land\u0131\u011f\u0131ndan emin olmal\u0131y\u0131z:<\/span><\/p>\n

1. Do\u011frusal ili\u015fki:<\/strong> Ba\u011f\u0131ms\u0131z de\u011fi\u015fken x ile ba\u011f\u0131ml\u0131 de\u011fi\u015fken y aras\u0131nda do\u011frusal bir ili\u015fki vard\u0131r.<\/span><\/p>\n

2. Ba\u011f\u0131ms\u0131zl\u0131k:<\/strong> Art\u0131klar ba\u011f\u0131ms\u0131zd\u0131r. \u00d6zellikle zaman serisi verilerinde ard\u0131\u015f\u0131k art\u0131klar aras\u0131nda bir korelasyon yoktur.<\/span><\/p>\n

3. Homoskedastisite:<\/strong> Art\u0131klar x’in her seviyesinde sabit bir varyansa sahiptir.<\/span><\/p>\n

4. Normallik:<\/strong> Model art\u0131klar\u0131 normal da\u011f\u0131l\u0131ma sahiptir.<\/span><\/p>\n

Bu varsay\u0131mlardan bir veya daha fazlas\u0131 kar\u015f\u0131lanmazsa, do\u011frusal regresyonumuzun sonu\u00e7lar\u0131 g\u00fcvenilmez ve hatta yan\u0131lt\u0131c\u0131 olabilir.<\/span><\/p>\n

Bu yaz\u0131da her bir varsay\u0131m i\u00e7in bir a\u00e7\u0131klama, varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131n\u0131n nas\u0131l belirlenece\u011fi ve varsay\u0131m kar\u015f\u0131lanmazsa ne yap\u0131laca\u011f\u0131na dair a\u00e7\u0131klamalar sunuyoruz.<\/span><\/p>\n

Hipotez 1: Do\u011frusal ili\u015fki<\/strong><\/span><\/h2>\n

A\u00e7\u0131klama<\/strong><\/span><\/h3>\n

Do\u011frusal regresyonun ilk varsay\u0131m\u0131, ba\u011f\u0131ms\u0131z de\u011fi\u015fken x ile ba\u011f\u0131ms\u0131z de\u011fi\u015fken y aras\u0131nda do\u011frusal bir ili\u015fki oldu\u011fudur.<\/span><\/p>\n

Bu varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131 nas\u0131l belirlenir<\/strong><\/h3>\n

Bu varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131n\u0131 tespit etmenin en basit yolu, x’e kar\u015f\u0131 y’nin da\u011f\u0131l\u0131m grafi\u011fini olu\u015fturmakt\u0131r. Bu, iki de\u011fi\u015fken aras\u0131nda do\u011frusal bir ili\u015fki olup olmad\u0131\u011f\u0131n\u0131 g\u00f6rsel olarak g\u00f6rmenizi sa\u011flar. Grafikteki noktalar\u0131n d\u00fcz bir \u00e7izgi boyunca uzand\u0131\u011f\u0131 g\u00f6r\u00fcl\u00fcyorsa, o zaman iki de\u011fi\u015fken aras\u0131nda bir t\u00fcr do\u011frusal ili\u015fki vard\u0131r ve bu varsay\u0131m kar\u015f\u0131lan\u0131r.<\/span><\/p>\n

\u00d6rne\u011fin, a\u015fa\u011f\u0131daki grafikteki noktalar\u0131n d\u00fcz bir \u00e7izgi \u00fczerinde d\u00fc\u015fmesi x ile y aras\u0131nda do\u011frusal bir ili\u015fki oldu\u011funu g\u00f6sterir:<\/span> <\/p>\n

\"\"<\/p>\n

Ancak a\u015fa\u011f\u0131daki grafikte x ile y aras\u0131nda do\u011frusal bir ili\u015fki g\u00f6r\u00fcnm\u00fcyor:<\/span> <\/p>\n

\"\"<\/p>\n

Ve bu grafikte x ile y aras\u0131nda net bir ili\u015fki var gibi g\u00f6r\u00fcn\u00fcyor ancak do\u011frusal bir ili\u015fki yok<\/em> :<\/span> <\/p>\n

\"\"<\/p>\n

Bu varsay\u0131ma uyulmazsa ne yap\u0131lmal\u0131?<\/strong><\/h3>\n

X ve y i\u00e7in de\u011ferlerin da\u011f\u0131l\u0131m grafi\u011fini olu\u015fturursan\u0131z ve iki de\u011fi\u015fken aras\u0131nda do\u011frusal bir ili\u015fki olmad\u0131\u011f\u0131n\u0131<\/em> tespit ederseniz, birka\u00e7 se\u00e7ene\u011finiz vard\u0131r:<\/span><\/p>\n

1.<\/strong> Ba\u011f\u0131ms\u0131z ve\/veya ba\u011f\u0131ml\u0131 de\u011fi\u015fkene do\u011frusal olmayan bir d\u00f6n\u00fc\u015f\u00fcm uygulay\u0131n. Yayg\u0131n \u00f6rnekler aras\u0131nda ba\u011f\u0131ms\u0131z ve\/veya ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin logunun, karek\u00f6k\u00fcn\u00fcn veya tersinin al\u0131nmas\u0131 yer al\u0131r.<\/span><\/p>\n

2.<\/strong> Modele ba\u015fka bir ba\u011f\u0131ms\u0131z de\u011fi\u015fken ekleyin. \u00d6rne\u011fin, x’e kar\u015f\u0131 y’nin grafi\u011fi parabolik bir \u015fekle sahipse, modele ek bir ba\u011f\u0131ms\u0131z de\u011fi\u015fken olarak X 2’yi<\/sup> eklemek mant\u0131kl\u0131 olabilir.<\/span><\/p>\n

Hipotez 2: Ba\u011f\u0131ms\u0131zl\u0131k<\/strong><\/span><\/h2>\n

A\u00e7\u0131klama<\/strong><\/span><\/h3>\n

Do\u011frusal regresyonun bir sonraki varsay\u0131m\u0131, art\u0131klar\u0131n ba\u011f\u0131ms\u0131z olmas\u0131d\u0131r. Bu \u00f6zellikle zaman serisi verileriyle \u00e7al\u0131\u015f\u0131rken ge\u00e7erlidir. \u0130deal olarak ard\u0131\u015f\u0131k art\u0131klar aras\u0131nda bir trend olmas\u0131n\u0131 istemeyiz. \u00d6rne\u011fin kal\u0131nt\u0131lar\u0131n zamanla s\u00fcrekli olarak artmamas\u0131 gerekir.<\/span><\/p>\n

Bu varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131 nas\u0131l belirlenir<\/strong><\/h3>\n

Bu varsay\u0131m\u0131n ge\u00e7erli olup olmad\u0131\u011f\u0131n\u0131 test etmenin en basit yolu, art\u0131klar\u0131n zamana kar\u015f\u0131 grafi\u011fi olan, art\u0131klar\u0131n zaman serisi grafi\u011fine bakmakt\u0131r. \u0130deal olarak, art\u0131k otokorelasyonlar\u0131n \u00e7o\u011fu, n’nin<\/em> \u00f6rnek boyutu oldu\u011fu n’nin<\/em> karek\u00f6k\u00fcnde yakla\u015f\u0131k olarak +\/- 2’de yer alan s\u0131f\u0131r \u00e7evresindeki %95 g\u00fcven bantlar\u0131 i\u00e7erisine d\u00fc\u015fmelidir. Ayr\u0131ca Durbin-Watson testini<\/a> kullanarak bu varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131n\u0131 resmi olarak test edebilirsiniz.<\/span><\/p>\n

Bu varsay\u0131ma uyulmazsa ne yap\u0131lmal\u0131?<\/strong><\/h3>\n

Bu varsay\u0131m\u0131n nas\u0131l ihlal edildi\u011fine ba\u011fl\u0131 olarak birka\u00e7 se\u00e7ene\u011finiz vard\u0131r:<\/span><\/p>\n

    \n
  • Pozitif seri korelasyon i\u00e7in ba\u011f\u0131ml\u0131 ve\/veya ba\u011f\u0131ms\u0131z de\u011fi\u015fkenin gecikmelerini modele eklemeyi d\u00fc\u015f\u00fcn\u00fcn.<\/span><\/li>\n
  • Negatif seri korelasyon i\u00e7in de\u011fi\u015fkenlerinizden hi\u00e7birinin a\u015f\u0131r\u0131 gecikmedi\u011finden<\/em> emin olun.<\/span><\/li>\n
  • Mevsimsel korelasyon i\u00e7in modele mevsimsel kuklalar eklemeyi d\u00fc\u015f\u00fcn\u00fcn.<\/span><\/li>\n<\/ul>\n

    Hipotez 3: Homoskedasticity<\/strong><\/span><\/h2>\n

    A\u00e7\u0131klama<\/strong><\/span><\/h3>\n

    Do\u011frusal regresyonun bir sonraki varsay\u0131m\u0131, art\u0131klar\u0131n her x seviyesinde sabit varyansa sahip oldu\u011fudur. Buna homoskedastisite<\/em> denir. Durum b\u00f6yle olmad\u0131\u011f\u0131nda, art\u0131klar de\u011fi\u015fen varyanstan<\/em> muzdariptir.<\/span><\/p>\n

    Bir regresyon analizinde de\u011fi\u015fen varyans mevcut oldu\u011funda, analiz sonu\u00e7lar\u0131na inanmak zorla\u015f\u0131r. Spesifik olarak, heteroskedastisite, regresyon katsay\u0131s\u0131 tahminlerinin varyans\u0131n\u0131 artt\u0131r\u0131r, ancak regresyon modeli bunu hesaba katmaz. Bu, ger\u00e7ekte \u00f6yle olmad\u0131\u011f\u0131 halde, bir regresyon modelinin, modeldeki bir terimin istatistiksel olarak anlaml\u0131 oldu\u011funu iddia etme olas\u0131l\u0131\u011f\u0131n\u0131 \u00e7ok daha art\u0131r\u0131r.<\/span><\/p>\n

    Bu varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131 nas\u0131l belirlenir<\/strong><\/h3>\n

    Heteroskedasticity’yi tespit etmenin en kolay yolu uygun bir de\u011fer\/art\u0131k grafi\u011fi<\/em> olu\u015fturmakt\u0131r.<\/span><\/p>\n

    Bir veri k\u00fcmesine bir regresyon \u00e7izgisi yerle\u015ftirdikten sonra, modelin uydurulmu\u015f de\u011ferlerini, bu uydurulmu\u015f de\u011ferlerin art\u0131klar\u0131na kar\u015f\u0131 g\u00f6steren bir da\u011f\u0131l\u0131m grafi\u011fi olu\u015fturabilirsiniz. A\u015fa\u011f\u0131daki da\u011f\u0131l\u0131m grafi\u011fi, i\u00e7inde heteroskedastisitenin mevcut oldu\u011fu art\u0131k de\u011fere kar\u015f\u0131 uygun de\u011ferin tipik bir grafi\u011fini<\/em> g\u00f6stermektedir.<\/span><\/p>\n

    Tak\u0131lan de\u011ferler artt\u0131k\u00e7a art\u0131klar\u0131n nas\u0131l daha fazla yay\u0131ld\u0131\u011f\u0131na dikkat edin. Bu \u201ckoni\u201d \u015fekli de\u011fi\u015fen varyans\u0131n klasik bir i\u015faretidir:<\/span><\/p>\n

    Bu varsay\u0131ma uyulmazsa ne yap\u0131lmal\u0131?<\/strong><\/h3>\n

    Heteroskedasticity’yi d\u00fczeltmenin \u00fc\u00e7 yayg\u0131n yolu vard\u0131r:<\/span><\/p>\n

    1. Ba\u011f\u0131ml\u0131 de\u011fi\u015fkeni d\u00f6n\u00fc\u015ft\u00fcr\u00fcn.<\/strong> Yayg\u0131n bir d\u00f6n\u00fc\u015f\u00fcm, ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin logunu almakt\u0131r. \u00d6rne\u011fin, bir \u015fehirdeki \u00e7i\u00e7ek\u00e7i say\u0131s\u0131n\u0131 (ba\u011f\u0131ml\u0131 de\u011fi\u015fken) tahmin etmek i\u00e7in n\u00fcfus b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc (ba\u011f\u0131ms\u0131z de\u011fi\u015fken) kullan\u0131rsak, bunun yerine bir kasabadaki \u00e7i\u00e7ek\u00e7i say\u0131s\u0131n\u0131n logaritmas\u0131n\u0131 tahmin etmek i\u00e7in n\u00fcfus b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc kullanmay\u0131 deneyebiliriz. Orijinal ba\u011f\u0131ml\u0131 de\u011fi\u015fken yerine ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin logunun kullan\u0131lmas\u0131 \u00e7o\u011fu zaman de\u011fi\u015fen varyans\u0131n ortadan kalkmas\u0131yla sonu\u00e7lan\u0131r.<\/span><\/p>\n

    2. Ba\u011f\u0131ml\u0131 de\u011fi\u015fkeni yeniden tan\u0131mlay\u0131n.<\/strong> Ba\u011f\u0131ml\u0131 de\u011fi\u015fkeni yeniden tan\u0131mlaman\u0131n yayg\u0131n bir yolu, ham de\u011fer yerine bir oran<\/em> kullanmakt\u0131r. \u00d6rne\u011fin, bir \u015fehirdeki \u00e7i\u00e7ek\u00e7i say\u0131s\u0131n\u0131 tahmin etmek i\u00e7in n\u00fcfus b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc kullanmak yerine, ki\u015fi ba\u015f\u0131na d\u00fc\u015fen \u00e7i\u00e7ek\u00e7i say\u0131s\u0131n\u0131 tahmin etmek i\u00e7in n\u00fcfus b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fc kullanabiliriz. \u00c7o\u011fu durumda bu, \u00e7i\u00e7ek\u00e7ilerin say\u0131s\u0131ndan ziyade ki\u015fi ba\u015f\u0131na d\u00fc\u015fen \u00e7i\u00e7ek\u00e7ilerin say\u0131s\u0131n\u0131 \u00f6l\u00e7t\u00fc\u011f\u00fcm\u00fcz i\u00e7in daha b\u00fcy\u00fck pop\u00fclasyonlarda do\u011fal olarak olu\u015fan de\u011fi\u015fkenli\u011fi azalt\u0131r.<\/span><\/p>\n

    3. A\u011f\u0131rl\u0131kl\u0131 regresyon kullan\u0131n.<\/strong> Heteroskedasticity’yi d\u00fczeltmenin ba\u015fka bir yolu da a\u011f\u0131rl\u0131kl\u0131 regresyon kullanmakt\u0131r. Bu regresyon t\u00fcr\u00fc, her veri noktas\u0131na, uydurulan de\u011ferin varyans\u0131na ba\u011fl\u0131 olarak bir a\u011f\u0131rl\u0131k atar. Temel olarak bu, daha y\u00fcksek varyansa sahip veri noktalar\u0131na d\u00fc\u015f\u00fck a\u011f\u0131rl\u0131k vererek bunlar\u0131n kalan karelerini azalt\u0131r. Uygun a\u011f\u0131rl\u0131klar kullan\u0131ld\u0131\u011f\u0131nda de\u011fi\u015fen varyans sorunu ortadan kald\u0131r\u0131labilir.<\/span><\/p>\n

    Hipotez 4: normallik<\/strong><\/span><\/h2>\n

    A\u00e7\u0131klama<\/strong><\/span><\/h3>\n

    Do\u011frusal regresyonun bir sonraki varsay\u0131m\u0131, art\u0131klar\u0131n normal \u015fekilde da\u011f\u0131ld\u0131\u011f\u0131d\u0131r.<\/span><\/p>\n

    Bu varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131 nas\u0131l belirlenir<\/strong><\/h3>\n

    Bu varsay\u0131m\u0131n kar\u015f\u0131lan\u0131p kar\u015f\u0131lanmad\u0131\u011f\u0131n\u0131 kontrol etmenin iki yayg\u0131n yolu vard\u0131r:<\/span><\/p>\n

    1. QQ grafiklerini kullanarak<\/strong> hipotezi g\u00f6rsel olarak do\u011frulay\u0131n<\/span> .<\/p>\n

    Kantil-kantil grafi\u011finin k\u0131saltmas\u0131 olan QQ grafi\u011fi, bir modelin art\u0131klar\u0131n\u0131n normal da\u011f\u0131l\u0131ma uyup uymad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in kullanabilece\u011fimiz bir grafik t\u00fcr\u00fcd\u00fcr. Grafikteki noktalar kabaca d\u00fcz bir \u00e7apraz \u00e7izgi olu\u015fturuyorsa normallik varsay\u0131m\u0131 kar\u015f\u0131lanm\u0131\u015ft\u0131r.<\/span><\/p>\n

    A\u015fa\u011f\u0131daki QQ grafi\u011fi kabaca normal da\u011f\u0131l\u0131ma uyan bir kal\u0131nt\u0131 \u00f6rne\u011fini g\u00f6stermektedir:<\/span><\/p>\n

    Bununla birlikte, a\u015fa\u011f\u0131daki QQ grafi\u011fi, art\u0131klar\u0131n d\u00fcz bir \u00e7apraz \u00e7izgiden a\u00e7\u0131k\u00e7a sapt\u0131\u011f\u0131 ve normal da\u011f\u0131l\u0131ma uymad\u0131klar\u0131n\u0131 g\u00f6steren bir durumun \u00f6rne\u011fini g\u00f6stermektedir:<\/span><\/p>\n

    2.<\/strong> Normallik varsay\u0131m\u0131n\u0131 ayr\u0131ca Shapiro-Wilk, Kolmogorov-Smironov, Jarque-Barre veya D’Agostino-Pearson gibi resmi istatistiksel testleri kullanarak da kontrol edebilirsiniz. Ancak, bu testlerin b\u00fcy\u00fck \u00f6rneklem boyutlar\u0131na duyarl\u0131 oldu\u011funu unutmay\u0131n; yani, \u00f6rneklem boyutunuz b\u00fcy\u00fck oldu\u011funda genellikle art\u0131klar\u0131n normal olmad\u0131\u011f\u0131 sonucuna var\u0131rlar. Bu hipotezi do\u011frulamak i\u00e7in QQ grafi\u011fi gibi grafiksel y\u00f6ntemleri kullanman\u0131n genellikle daha kolay olmas\u0131n\u0131n nedeni budur.<\/span><\/p>\n

    Bu varsay\u0131ma uyulmazsa ne yap\u0131lmal\u0131?<\/strong><\/h3>\n

    Normallik varsay\u0131m\u0131 kar\u015f\u0131lanmazsa birka\u00e7 se\u00e7ene\u011finiz vard\u0131r:<\/span><\/p>\n

      \n
    • \u00d6ncelikle ayk\u0131r\u0131 de\u011ferlerin da\u011f\u0131t\u0131m \u00fczerinde b\u00fcy\u00fck bir etkisinin olup olmad\u0131\u011f\u0131n\u0131 kontrol edin. Ayk\u0131r\u0131 de\u011ferler varsa bunlar\u0131n veri giri\u015fi hatas\u0131 de\u011fil, ger\u00e7ek de\u011ferler oldu\u011fundan emin olun.<\/span><\/li>\n
    • Daha sonra ba\u011f\u0131ms\u0131z ve\/veya ba\u011f\u0131ml\u0131 de\u011fi\u015fkene do\u011frusal olmayan bir d\u00f6n\u00fc\u015f\u00fcm uygulayabilirsiniz. Yayg\u0131n \u00f6rnekler aras\u0131nda ba\u011f\u0131ms\u0131z ve\/veya ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin logunun, karek\u00f6k\u00fcn\u00fcn veya tersinin al\u0131nmas\u0131 yer al\u0131r.<\/span><\/li>\n<\/ul>\n

      Daha fazla okuma:<\/strong><\/span><\/p>\n

      Basit Do\u011frusal Regresyona Giri\u015f
      Regresyon Analizinde Heteroskedastisiteyi Anlamak
      R’de QQ grafi\u011fi nas\u0131l olu\u015fturulur ve yorumlan\u0131r<\/p>\n","protected":false},"excerpt":{"rendered":"

      Do\u011frusal regresyon , iki de\u011fi\u015fken (x ve y) aras\u0131ndaki ili\u015fkiyi anlamak i\u00e7in kullanabilece\u011fimiz yararl\u0131 bir istatistiksel y\u00f6ntemdir. Ancak do\u011frusal regresyon ger\u00e7ekle\u015ftirmeden \u00f6nce d\u00f6rt varsay\u0131m\u0131n kar\u015f\u0131land\u0131\u011f\u0131ndan emin olmal\u0131y\u0131z: 1. Do\u011frusal ili\u015fki: Ba\u011f\u0131ms\u0131z de\u011fi\u015fken x ile ba\u011f\u0131ml\u0131 de\u011fi\u015fken y aras\u0131nda do\u011frusal bir ili\u015fki vard\u0131r. 2. Ba\u011f\u0131ms\u0131zl\u0131k: Art\u0131klar ba\u011f\u0131ms\u0131zd\u0131r. \u00d6zellikle zaman serisi verilerinde ard\u0131\u015f\u0131k art\u0131klar aras\u0131nda bir korelasyon […]<\/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-551","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\nDo\u011frusal regresyonun d\u00f6rt hipotezi - Statorials<\/title>\n<meta name=\"description\" content=\"Do\u011frusal regresyonun d\u00f6rt varsay\u0131m\u0131n\u0131n basit bir a\u00e7\u0131klamas\u0131 ve bu varsay\u0131mlardan herhangi birinin ihlal edilmesi durumunda ne yapman\u0131z gerekti\u011fi.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/tr\/dogrusal-regresyon-varsayimlari\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Do\u011frusal regresyonun d\u00f6rt hipotezi - 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