{"id":476397,"date":"2023-08-09T07:28:31","date_gmt":"2023-08-09T07:28:31","guid":{"rendered":""},"modified":"2023-09-05T11:12:38","modified_gmt":"2023-09-05T11:12:38","slug":"confidence-interval","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/confidence-interval\/","title":{"rendered":"G\u00fcven aral\u0131\u011f\u0131"},"content":{"rendered":"

G\u00fcven Aral\u0131\u011f\u0131 (CI), o pop\u00fclasyondan al\u0131nan bir \u00f6rne\u011fe dayal\u0131 olarak bilinmeyen bir pop\u00fclasyon parametresi i\u00e7in olas\u0131 de\u011fer aral\u0131\u011f\u0131n\u0131 tahmin etmek i\u00e7in kullan\u0131lan istatistiksel bir kavramd\u0131r. Parametrenin ger\u00e7ek de\u011ferinin belirli bir g\u00fcven d\u00fczeyinde d\u00fc\u015febilece\u011fi bir aral\u0131k sa\u011flar. G\u00fcven aral\u0131klar\u0131 ekonomi, sosyal bilimler, t\u0131p ve m\u00fchendislik gibi \u00e7e\u015fitli alanlarda n\u00fcfus parametreleri hakk\u0131nda \u00e7\u0131kar\u0131mlarda bulunmak ve istatistiksel tahminlerdeki belirsizli\u011fi \u00f6l\u00e7mek i\u00e7in yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/p>\n

G\u00fcven Aral\u0131\u011f\u0131n\u0131n k\u00f6keninin tarihi ve ilk s\u00f6z\u00fc<\/h2>\n

G\u00fcven Aral\u0131\u011f\u0131 kavram\u0131n\u0131n k\u00f6keni, Frans\u0131z matematik\u00e7i ve g\u00f6kbilimci Pierre-Simon Laplace'\u0131n 18. y\u00fczy\u0131l\u0131n sonlar\u0131 ve 19. y\u00fczy\u0131l\u0131n ba\u015flar\u0131ndaki \u00e7al\u0131\u015fmalar\u0131na kadar uzanabilir. Laplace olas\u0131l\u0131k teorisi ve istatistik alan\u0131ndaki \u00f6nc\u00fclerden biriydi. Bir parametrenin ger\u00e7ek de\u011ferini tahmin etmek i\u00e7in g\u00f6zlemlenen verileri kullanma fikrini ortaya att\u0131 ve bir parametrenin belirli bir de\u011fer aral\u0131\u011f\u0131nda kalma olas\u0131l\u0131\u011f\u0131n\u0131 hesaplamak i\u00e7in bir y\u00f6ntem \u00f6nerdi. Ancak \u201cG\u00fcven Aral\u0131\u011f\u0131\u201d terimi 20. y\u00fczy\u0131l\u0131n sonlar\u0131nda icat edildi.<\/p>\n

G\u00fcven Aral\u0131\u011f\u0131 hakk\u0131nda detayl\u0131 bilgi<\/h2>\n

G\u00fcven Aral\u0131klar\u0131n\u0131 daha iyi anlamak i\u00e7in \u00f6rnekleme de\u011fi\u015fkenli\u011fi kavram\u0131n\u0131 kavramak \u00f6nemlidir. Bir pop\u00fclasyondan bir \u00f6rnek ald\u0131\u011f\u0131m\u0131zda ve bu \u00f6rnekten bir istatistik (\u00f6rne\u011fin, ortalama, oran, standart sapma) hesaplad\u0131\u011f\u0131m\u0131zda, istatisti\u011fin de\u011feri, rastgele \u00f6rnekleme varyasyonlar\u0131ndan dolay\u0131 muhtemelen ger\u00e7ek pop\u00fclasyon parametresinden farkl\u0131 olacakt\u0131r. G\u00fcven aral\u0131klar\u0131 bu de\u011fi\u015fkenli\u011fi hesaba katar ve ger\u00e7ek parametreyi i\u00e7ermesi muhtemel bir de\u011fer aral\u0131\u011f\u0131 sa\u011flar.<\/p>\n

G\u00fcven Aral\u0131\u011f\u0131n\u0131 hesaplaman\u0131n standart yolu, \u00f6rnek istatisti\u011finin normal bir da\u011f\u0131l\u0131m izledi\u011fi varsay\u0131m\u0131na dayan\u0131r. \u00d6rne\u011fin, bir G\u00fcven Aral\u0131\u011f\u0131 ile pop\u00fclasyon ortalamas\u0131n\u0131 tahmin etmek i\u00e7in genellikle a\u015fa\u011f\u0131daki form\u00fcl kullan\u0131l\u0131r:<\/p>\n

G\u00fcven aral\u0131\u011f\u0131<\/mtext>=<\/mo>\u00d6rnek Ortalama<\/mtext>\u00b1<\/mo>Hata Marj\u0131<\/mtext><\/mrow>text{G\u00fcven Aral\u0131\u011f\u0131} = text{\u00d6rnek Ortalama} pm text{Hata Marj\u0131}<\/annotation><\/semantics><\/math><\/span><\/span>G\u00fcven aral\u0131\u011f\u0131<\/span><\/span><\/span>=<\/span><\/span><\/span><\/span>\u00d6rnek Ortalama<\/span><\/span><\/span>\u00b1<\/span><\/span><\/span><\/span>Hata Marj\u0131<\/span><\/span><\/span><\/span><\/span><\/span><\/p>\n

Hata Marj\u0131, istenen g\u00fcven d\u00fczeyine (\u00f6rn. 95%, 99%) ve numunenin standart sapmas\u0131na veya di\u011fer ilgili parametrelere g\u00f6re belirlenir.<\/p>\n

G\u00fcven Aral\u0131\u011f\u0131n\u0131n i\u00e7 yap\u0131s\u0131. G\u00fcven Aral\u0131\u011f\u0131 nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h2>\n

G\u00fcven Aral\u0131\u011f\u0131 iki ana bile\u015fenden olu\u015fur: nokta tahmini (\u00f6rnek istatisti\u011fi) ve hata marj\u0131. Nokta tahmini, \u00f6rnek verilerden hesaplanan de\u011feri temsil ederken hata pay\u0131, tahmin s\u00fcreciyle ili\u015fkili belirsizlik ve de\u011fi\u015fkenli\u011fi hesaba katar.<\/p>\n

\u00d6rne\u011fin, bir ara\u015ft\u0131rma \u00e7al\u0131\u015fmas\u0131n\u0131n bir kafeyi ziyaret eden m\u00fc\u015fterilerin ortalama ya\u015f\u0131n\u0131 tahmin etmeyi ama\u00e7lad\u0131\u011f\u0131n\u0131 varsayal\u0131m. 100 m\u00fc\u015fteriden olu\u015fan bir \u00f6rneklem al\u0131nm\u0131\u015f ve ortalama ya\u015flar\u0131n\u0131n 35 oldu\u011fu bulunmu\u015ftur. \u015eimdi ara\u015ft\u0131rmac\u0131lar, t\u00fcm m\u00fc\u015fterilerin ger\u00e7ek ortalama ya\u015f\u0131 i\u00e7in 95% G\u00fcven Aral\u0131\u011f\u0131n\u0131 belirlemek istiyor. Hesaplanan hata pay\u0131 \u00b13 y\u0131l ise 95% G\u00fcven Aral\u0131\u011f\u0131 (32, 38) y\u0131l olacakt\u0131r. Bu, t\u00fcm m\u00fc\u015fterilerin ger\u00e7ek ortalama ya\u015f\u0131n\u0131n bu aral\u0131kta oldu\u011fundan 95% emin olabilece\u011fimiz anlam\u0131na gelir.<\/p>\n

G\u00fcven Aral\u0131\u011f\u0131n\u0131n temel \u00f6zelliklerinin analizi<\/h2>\n

G\u00fcven Aral\u0131klar\u0131, onlar\u0131 istatistiksel \u00e7\u0131kar\u0131mda \u00f6nemli k\u0131lan \u00e7e\u015fitli temel \u00f6zellikler sunar:<\/p>\n

    \n
  1. \n

    Belirsizli\u011fin \u00d6l\u00e7\u00fclmesi<\/strong>: G\u00fcven Aral\u0131klar\u0131 \u00f6rnek tahminlerle ili\u015fkili belirsizli\u011fin bir \u00f6l\u00e7\u00fcs\u00fcn\u00fc sa\u011flar. Pop\u00fclasyon parametresinin muhtemelen i\u00e7inde bulunaca\u011f\u0131 aral\u0131\u011f\u0131 iletirler.<\/p>\n<\/li>\n

  2. \n

    G\u00fcven seviyesi<\/strong>: Kullan\u0131c\u0131 gereken g\u00fcven d\u00fczeyini se\u00e7ebilir. Yayg\u0131n olarak kullan\u0131lan seviyeler 90%, 95% ve 99%'dir; burada daha y\u00fcksek bir g\u00fcven seviyesi daha geni\u015f bir aral\u0131k anlam\u0131na gelir.<\/p>\n<\/li>\n

  3. \n

    \u00d6rnek Boyutu Ba\u011f\u0131ml\u0131l\u0131\u011f\u0131<\/strong>: G\u00fcven Aral\u0131klar\u0131 \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fcnden etkilenir; daha b\u00fcy\u00fck \u00f6rnekler, \u00f6rnekleme de\u011fi\u015fkenli\u011fini azaltt\u0131klar\u0131 i\u00e7in genellikle daha dar aral\u0131klar sa\u011flar.<\/p>\n<\/li>\n

  4. \n

    Da\u011f\u0131t\u0131m Varsay\u0131m\u0131<\/strong>: G\u00fcven Aral\u0131klar\u0131n\u0131n Hesaplanmas\u0131 genellikle \u00f6rnek istatisti\u011finin da\u011f\u0131l\u0131m\u0131 hakk\u0131nda varsay\u0131mlar gerektirir; genellikle normal bir da\u011f\u0131l\u0131m varsay\u0131l\u0131r.<\/p>\n<\/li>\n

  5. \n

    Yorumlanabilirlik<\/strong>: G\u00fcven Aral\u0131klar\u0131, belirsizli\u011fin anla\u015f\u0131lmas\u0131 kolay bir temsilini sa\u011flayarak, bunlar\u0131 geni\u015f bir kullan\u0131c\u0131 yelpazesi i\u00e7in eri\u015filebilir k\u0131lar.<\/p>\n<\/li>\n<\/ol>\n

    G\u00fcven Aral\u0131\u011f\u0131 T\u00fcrleri<\/h2>\n

    G\u00fcven Aral\u0131klar\u0131, tahmin edilen pop\u00fclasyon parametresinin t\u00fcr\u00fcne ve \u00f6rnek verilerin niteli\u011fine g\u00f6re s\u0131n\u0131fland\u0131r\u0131labilir. \u0130\u015fte baz\u0131 yayg\u0131n t\u00fcrler:<\/p>\n\n\n\n\n\n\n\n\n\n
    G\u00fcven Aral\u0131\u011f\u0131 T\u00fcr\u00fc<\/th>\nTan\u0131m<\/th>\n<\/tr>\n<\/thead>\n
    Ortalama G\u00fcven Aral\u0131\u011f\u0131<\/strong><\/td>\n\u00d6rnek ortalamas\u0131n\u0131 temel alarak pop\u00fclasyon ortalamas\u0131n\u0131 tahmin etmek i\u00e7in kullan\u0131l\u0131r.<\/td>\n<\/tr>\n
    Oran G\u00fcven Aral\u0131\u011f\u0131<\/strong><\/td>\nGenellikle binom verilerinde kullan\u0131lan \u00f6rnek oranlar\u0131na dayal\u0131 olarak pop\u00fclasyon oran\u0131n\u0131 tahmin eder.<\/td>\n<\/tr>\n
    Varyans G\u00fcven Aral\u0131\u011f\u0131<\/strong><\/td>\nPop\u00fclasyon varyans\u0131n\u0131 veya standart sapmay\u0131 tahmin eder.<\/td>\n<\/tr>\n
    Ara\u00e7lar Aras\u0131ndaki Fark<\/strong><\/td>\n\u0130ki farkl\u0131 grup veya pop\u00fclasyonun ortalamalar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in kullan\u0131l\u0131r.<\/td>\n<\/tr>\n
    Regresyon Katsay\u0131s\u0131 G\u00fcven Aral\u0131\u011f\u0131<\/strong><\/td>\nRegresyon modellerinde bilinmeyen katsay\u0131lar\u0131 tahmin eder.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    G\u00fcven Aral\u0131\u011f\u0131n\u0131 kullanma yollar\u0131, kullan\u0131ma ili\u015fkin sorunlar ve \u00e7\u00f6z\u00fcmleri<\/h2>\n

    1. Hipotez Testi<\/strong>: G\u00fcven Aral\u0131klar\u0131 hipotez testiyle yak\u0131ndan ilgilidir. Pop\u00fclasyon parametreleri hakk\u0131ndaki hipotezleri test etmek i\u00e7in kullan\u0131labilirler. Varsay\u0131lan bir de\u011fer G\u00fcven Aral\u0131\u011f\u0131n\u0131n d\u0131\u015f\u0131na \u00e7\u0131karsa, bu \u00f6nemli bir fark veya etki anlam\u0131na gelebilir.<\/p>\n

    2. \u00d6rneklem B\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fcn Belirlenmesi<\/strong>: G\u00fcven Aral\u0131klar\u0131 bir \u00e7al\u0131\u015fma i\u00e7in gerekli \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fcn belirlenmesinde yard\u0131mc\u0131 olabilir. Daha dar bir aral\u0131k, ayn\u0131 g\u00fcven d\u00fczeyine ula\u015fmak i\u00e7in daha b\u00fcy\u00fck bir \u00f6rneklem b\u00fcy\u00fckl\u00fc\u011f\u00fc gerektirir.<\/p>\n

    3. Ayk\u0131r\u0131 De\u011ferler ve \u00c7arp\u0131k Veriler<\/strong>: Verilerin normal da\u011f\u0131lmad\u0131\u011f\u0131 veya ayk\u0131r\u0131 de\u011ferler i\u00e7erdi\u011fi durumlarda G\u00fcven Aral\u0131klar\u0131n\u0131 hesaplamak i\u00e7in \u00f6ny\u00fckleme gibi alternatif y\u00f6ntemler kullan\u0131labilir.<\/p>\n

    4. \u00d6rt\u00fc\u015fen Aral\u0131klar\u0131n Yorumlanmas\u0131<\/strong>: Birden fazla grubu veya ko\u015fulu kar\u015f\u0131la\u015ft\u0131r\u0131rken, G\u00fcven Aral\u0131klar\u0131n\u0131n \u00e7ak\u0131\u015fmas\u0131 mutlaka bir \u00f6nem eksikli\u011fi anlam\u0131na gelmez. Uygun kar\u015f\u0131la\u015ft\u0131rmalar i\u00e7in resmi hipotez testleri yap\u0131lmal\u0131d\u0131r.<\/p>\n

    Ana \u00f6zellikler ve benzer terimlerle di\u011fer kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n\n\n\n\n\n\n\n
    Terim<\/th>\nTan\u0131m<\/th>\n<\/tr>\n<\/thead>\n
    G\u00fcven aral\u0131\u011f\u0131<\/td>\nBelirli bir g\u00fcven d\u00fczeyiyle ger\u00e7ek parametre de\u011ferini muhtemelen i\u00e7eren bir de\u011fer aral\u0131\u011f\u0131 sa\u011flar.<\/td>\n<\/tr>\n
    Tahmin Aral\u0131\u011f\u0131<\/td>\nG\u00fcven Aral\u0131\u011f\u0131na benzer ancak hem \u00f6rnekleme de\u011fi\u015fkenli\u011fini hem de gelecekteki tahmin hatalar\u0131n\u0131 hesaba katar. G\u00fcven Aral\u0131klar\u0131ndan daha geni\u015f.<\/td>\n<\/tr>\n
    Tolerans Aral\u0131\u011f\u0131<\/td>\nN\u00fcfusun belirli bir k\u0131sm\u0131n\u0131 belirli bir g\u00fcven d\u00fczeyiyle kapsayan bir de\u011fer aral\u0131\u011f\u0131n\u0131 belirtir. Kalite kontrol\u00fc i\u00e7in kullan\u0131l\u0131r.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    G\u00fcven Aral\u0131\u011f\u0131 ile ilgili gelece\u011fin perspektifleri ve teknolojileri<\/h2>\n

    \u0130statistik alan\u0131 s\u00fcrekli olarak geli\u015fmektedir ve G\u00fcven Aral\u0131\u011f\u0131 tekniklerinin gelecekte ilerlemeler g\u00f6rmesi muhtemeldir. Baz\u0131 potansiyel geli\u015fmeler \u015funlar\u0131 i\u00e7erir:<\/p>\n

      \n
    1. \n

      Parametrik Olmayan Y\u00f6ntemler<\/strong>: Parametrik olmayan istatistiklerdeki geli\u015fmeler, belirli veri da\u011f\u0131l\u0131mlar\u0131n\u0131 varsaymadan G\u00fcven Aral\u0131klar\u0131n\u0131 hesaplamak i\u00e7in alternatif yollar sa\u011flayabilir.<\/p>\n<\/li>\n

    2. \n

      Bayes \u00c7\u0131kar\u0131m\u0131<\/strong>: \u00d6n bilgileri ve g\u00fcncellenen inan\u00e7lar\u0131 birle\u015ftiren Bayes y\u00f6ntemleri, aral\u0131klar\u0131 olu\u015fturmak i\u00e7in daha esnek ve bilgilendirici yollar sunabilir.<\/p>\n<\/li>\n

    3. \n

      Makine \u00d6\u011frenimi Uygulamalar\u0131<\/strong>: Makine \u00f6\u011freniminin y\u00fckseli\u015fiyle birlikte G\u00fcven Aral\u0131klar\u0131, yapay zeka tabanl\u0131 karar verme sistemlerindeki belirsizli\u011fi tahmin etmek i\u00e7in model tahminlerine entegre edilebilir.<\/p>\n<\/li>\n<\/ol>\n

      Proxy sunucular\u0131 nas\u0131l kullan\u0131labilir veya G\u00fcven Aral\u0131\u011f\u0131 ile nas\u0131l ili\u015fkilendirilebilir?<\/h2>\n

      OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular\u0131, G\u00fcven Aral\u0131klar\u0131 olu\u015fturmak i\u00e7in veri toplamada \u00e7ok \u00f6nemli bir rol oynayabilir. B\u00fcy\u00fck \u00f6l\u00e7ekli veri toplama veya web kaz\u0131ma g\u00f6revleriyle u\u011fra\u015f\u0131rken, proxy sunucular\u0131n kullan\u0131lmas\u0131 IP engellemesinin \u00f6nlenmesine ve isteklerin farkl\u0131 IP adresleri aras\u0131nda da\u011f\u0131t\u0131lmas\u0131na yard\u0131mc\u0131 olarak \u00f6nyarg\u0131l\u0131 numune riskini azaltabilir. Ara\u015ft\u0131rmac\u0131lar, IP'leri proxy sunucular arac\u0131l\u0131\u011f\u0131yla d\u00f6nd\u00fcrerek veri toplaman\u0131n sa\u011flam ve tarafs\u0131z kalmas\u0131n\u0131 sa\u011flayabilir ve bu da daha do\u011fru G\u00fcven Aral\u0131klar\u0131na yol a\u00e7abilir.<\/p>\n

      \u0130lgili Ba\u011flant\u0131lar<\/h2>\n
        \n
      1. G\u00fcven Aral\u0131klar\u0131n\u0131 Anlamak \u2013 Khan Academy<\/a><\/li>\n
      2. G\u00fcven Aral\u0131\u011f\u0131 \u2013 Vikipedi<\/a><\/li>\n
      3. Bootstrap G\u00fcven Aral\u0131klar\u0131na Giri\u015f \u2013 Veri Bilimine Do\u011fru<\/a><\/li>\n<\/ol>\n

        Sonu\u00e7 olarak, G\u00fcven Aral\u0131klar\u0131 istatistiksel \u00e7\u0131kar\u0131mda temel bir ara\u00e7t\u0131r ve ara\u015ft\u0131rmac\u0131lara ve karar vericilere tahminleriyle ili\u015fkili belirsizlik hakk\u0131nda de\u011ferli bilgiler sa\u011flar. Akademik ara\u015ft\u0131rmalardan i\u015f analiti\u011fine kadar \u00e7e\u015fitli alanlarda kritik bir rol oynarlar ve \u00f6rnek verilere dayanarak bilin\u00e7li kararlar almak i\u00e7in bunlar\u0131n do\u011fru anla\u015f\u0131lmas\u0131 \u00f6nemlidir. \u0130statistiksel metodolojiler ve teknolojilerde devam eden geli\u015fmelerle birlikte G\u00fcven Aral\u0131klar\u0131, modern veri analizi ve karar verme s\u00fcre\u00e7lerinin temel ta\u015f\u0131 olmaya devam edecektir.<\/p>","protected":false},"featured_media":467989,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476397","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Confidence Interval<\/mark>","faq_items":[{"question":"What is a Confidence Interval?","answer":"

        A Confidence Interval (CI) is a statistical concept used to estimate the range of possible values for an unknown population parameter based on a sample from that population. It provides a level of confidence that the true value of the parameter lies within the calculated interval.<\/p>"},{"question":"Who introduced the concept of Confidence Interval?","answer":"

        The concept of Confidence Interval can be traced back to Pierre-Simon Laplace, a French mathematician and astronomer, in the late 18th and early 19th centuries. He laid the groundwork for using observed data to estimate population parameters and proposed a method to calculate the probability of a parameter falling within a certain range of values.<\/p>"},{"question":"How do Confidence Intervals work?","answer":"

        Confidence Intervals consist of a point estimate (sample statistic) and a margin of error. The point estimate represents the calculated value from the sample data, while the margin of error accounts for the uncertainty associated with the estimation process. The interval is determined by the desired level of confidence and the sample's standard deviation or other relevant parameters.<\/p>"},{"question":"What are the main types of Confidence Intervals?","answer":"

        There are several types of Confidence Intervals, depending on the parameter being estimated and the nature of the sample data. Common types include Mean, Proportion, Variance, Difference between Means, and Regression Coefficient Confidence Intervals.<\/p>"},{"question":"How are Confidence Intervals used in practice?","answer":"

        Confidence Intervals have numerous applications in statistics and data analysis. They are used for hypothesis testing, sample size determination, and making inferences about population parameters with a known level of confidence. They also help address problems related to skewed data or outliers and facilitate proper comparisons between multiple groups.<\/p>"},{"question":"How can proxy servers be associated with Confidence Intervals?","answer":"

        Proxy servers, like those provided by OneProxy, are valuable tools for data collection when constructing Confidence Intervals. They help prevent IP blocking during large-scale data gathering or web scraping tasks, ensuring unbiased samples and accurate interval estimations. By rotating IPs through proxy servers, researchers can enhance the robustness of their data collection process.<\/p>"},{"question":"What are the future perspectives of Confidence Intervals?","answer":"

        The field of statistics is continuously evolving, and Confidence Interval techniques are likely to see advancements in the future. Potential developments may include non-parametric methods, Bayesian inference, and integration with machine learning applications to estimate uncertainty in AI-based decision-making systems.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476397","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476397\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/467989"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=476397"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}