Bu e\u011fitimde, Excel’de \u00e7oklu do\u011frusal regresyon modelinin \u00e7\u0131kt\u0131s\u0131ndaki her bir de\u011ferin nas\u0131l yorumlanaca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n Diyelim ki, ders \u00e7al\u0131\u015fmak i\u00e7in harcanan saatlerin ve al\u0131nan haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131n say\u0131s\u0131n\u0131n, bir \u00f6\u011frencinin belirli bir \u00fcniversiteye giri\u015f s\u0131nav\u0131nda ald\u0131\u011f\u0131 notu etkileyip etkilemedi\u011fini bilmek istiyoruz.<\/span><\/p>\n Bu ili\u015fkiyi ara\u015ft\u0131rmak i\u00e7in, \u00e7al\u0131\u015f\u0131lan saatleri<\/strong> ve al\u0131nan haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131<\/strong> yorday\u0131c\u0131 de\u011fi\u015fkenler olarak ve s\u0131nav puanlar\u0131n\u0131<\/strong> yan\u0131t de\u011fi\u015fkeni olarak kullanarak \u00e7oklu do\u011frusal regresyon yapabiliriz.<\/span><\/p>\n A\u015fa\u011f\u0131daki ekran g\u00f6r\u00fcnt\u00fcs\u00fc bu modelin Excel’deki regresyon \u00e7\u0131kt\u0131s\u0131n\u0131 g\u00f6stermektedir:<\/span> <\/p>\n \u00c7\u0131kt\u0131daki en b\u00fcy\u00fck de\u011ferlerin nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n \u00c7oklu R: 0,857<\/strong> . Bu, yan\u0131t de\u011fi\u015fkeni ile iki yorday\u0131c\u0131 de\u011fi\u015fken aras\u0131ndaki \u00e7oklu korelasyonu temsil eder.<\/span><\/p>\n R Kare:<\/strong> 0,734<\/strong> . Buna belirleme katsay\u0131s\u0131 denir. A\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler taraf\u0131ndan a\u00e7\u0131klanabilen yan\u0131t de\u011fi\u015fkeninin varyans\u0131n\u0131n oran\u0131d\u0131r. Bu \u00f6rnekte s\u0131nav puanlar\u0131ndaki farkl\u0131l\u0131\u011f\u0131n %73,4’\u00fc \u00e7al\u0131\u015f\u0131lan saat ve girilen haz\u0131rl\u0131k s\u0131nav\u0131 say\u0131s\u0131yla a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n D\u00fczeltilmi\u015f R kare: 0,703<\/strong> . Bu, modeldeki yorday\u0131c\u0131 de\u011fi\u015fkenlerin say\u0131s\u0131na g\u00f6re ayarlanan<\/em> R Kare de\u011ferini temsil eder. Bu de\u011fer ayn\u0131 zamanda R Kare de\u011ferinden daha d\u00fc\u015f\u00fck olacak ve modelde \u00e7ok fazla \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fken kullanan modelleri cezaland\u0131racakt\u0131r.<\/span><\/p>\n Standart hata:<\/strong> 5,366<\/strong> . Bu, g\u00f6zlemlenen de\u011ferler ile regresyon \u00e7izgisi aras\u0131ndaki ortalama mesafedir. Bu \u00f6rnekte g\u00f6zlemlenen de\u011ferler regresyon do\u011frusundan ortalama 5.366 birim sapmaktad\u0131r.<\/span><\/p>\n Yorumlar: 20<\/strong> . Regresyon modelini olu\u015fturmak i\u00e7in kullan\u0131lan veri k\u00fcmesinin toplam \u00f6rnek boyutu.<\/span><\/p>\n F: 23:46<\/strong> Bu, regresyon modeli i\u00e7in regresyon MS\/art\u0131k MS olarak hesaplanan genel F istatisti\u011fidir.<\/span><\/p>\n Anlam\u0131 F: 0,0000<\/strong> . Bu, genel F istatisti\u011fiyle ili\u015fkili p de\u011feridir. Bu bize regresyon modelinin bir b\u00fct\u00fcn olarak istatistiksel olarak anlaml\u0131 olup olmad\u0131\u011f\u0131n\u0131 s\u00f6yler.<\/span><\/p>\n Bu durumda p de\u011feri 0,05’ten k\u00fc\u00e7\u00fckt\u00fcr; bu durum, a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenlerin , \u00e7al\u0131\u015f\u0131lan saatlerin<\/strong> ve girilen haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131n<\/strong> birle\u015fiminin s\u0131nav sonucuyla<\/strong> istatistiksel olarak anlaml\u0131 bir ili\u015fkiye sahip oldu\u011funu g\u00f6sterir.<\/span><\/p>\n Katsay\u0131lar:<\/strong> Her bir a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenin katsay\u0131lar\u0131, di\u011fer a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenin sabit kald\u0131\u011f\u0131 varsay\u0131larak, bize yan\u0131t de\u011fi\u015fkeninde beklenen ortalama de\u011fi\u015fimi s\u00f6yler.<\/span><\/p>\n \u00d6rne\u011fin, al\u0131nan deneme s\u0131navlar\u0131n\u0131n sabit kald\u0131\u011f\u0131 varsay\u0131larak, ders \u00e7al\u0131\u015fmak i\u00e7in<\/strong> harcanan her ek saat i\u00e7in ortalama s\u0131nav puan\u0131n\u0131n 5,56<\/strong> artmas\u0131 beklenir.<\/span><\/p>\n Kesi\u015fme katsay\u0131s\u0131n\u0131, ders saati \u00e7al\u0131\u015fmayan ve haz\u0131rl\u0131k s\u0131nav\u0131na girmeyen bir \u00f6\u011frenci i\u00e7in beklenen s\u0131nav puan\u0131n\u0131n 67,67<\/strong> oldu\u011fu \u015feklinde yorumluyoruz.<\/span><\/p>\n P de\u011ferleri.<\/strong> Bireysel p de\u011ferleri bize her a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenin istatistiksel olarak anlaml\u0131 olup olmad\u0131\u011f\u0131n\u0131 s\u00f6yler. \u00c7al\u0131\u015f\u0131lan saatlerin<\/strong> istatistiksel olarak anlaml\u0131 oldu\u011funu (p = 0,00), al\u0131nan haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131n<\/strong> (p = 0,52) \u03b1 = 0,05’te istatistiksel olarak anlaml\u0131 olmad\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n A\u015fa\u011f\u0131daki tahmini regresyon denklemini olu\u015fturmak i\u00e7in model \u00e7\u0131kt\u0131s\u0131ndaki katsay\u0131lar\u0131 kullanabiliriz:<\/span><\/p>\n S\u0131nav puan\u0131 = 67,67 + 5,56*(saat) \u2013 0,60*(haz\u0131rl\u0131k s\u0131navlar\u0131)<\/strong><\/span><\/p>\n Bu tahmini regresyon denklemini, bir \u00f6\u011frencinin \u00e7al\u0131\u015fma saati ve girdi\u011fi deneme s\u0131navlar\u0131n\u0131n say\u0131s\u0131na ba\u011fl\u0131 olarak beklenen s\u0131nav puan\u0131n\u0131 hesaplamak i\u00e7in kullanabiliriz.<\/span><\/p>\n \u00d6rne\u011fin \u00fc\u00e7 saat \u00e7al\u0131\u015f\u0131p haz\u0131rl\u0131k s\u0131nav\u0131na giren bir \u00f6\u011frencinin notunun 83,75<\/strong> olmas\u0131 gerekir:<\/span><\/p>\n S\u0131nav puan\u0131 = 67,67 + 5,56*(3) \u2013 0,60*(1) = 83,75<\/span><\/p>\n Ge\u00e7mi\u015f haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131n<\/strong> istatistiksel olarak anlaml\u0131 olmamas\u0131 (p=0,52) nedeniyle modelin geneline herhangi bir iyile\u015ftirme sa\u011flamamas\u0131 nedeniyle bunlar\u0131 kald\u0131rmaya karar verebilece\u011fimizi unutmay\u0131n.<\/span><\/p>\n Bu durumda a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken olarak yaln\u0131zca \u00e7al\u0131\u015f\u0131lan saatleri<\/strong> kullanarak basit bir do\u011frusal regresyon ger\u00e7ekle\u015ftirebiliriz.<\/span><\/p>\n Basit Do\u011frusal Regresyona Giri\u015f<\/a> \u00c7oklu do\u011frusal regresyon t\u00fcm istatistiklerde en s\u0131k kullan\u0131lan tekniklerden biridir. Bu e\u011fitimde, Excel’de \u00e7oklu do\u011frusal regresyon modelinin \u00e7\u0131kt\u0131s\u0131ndaki her bir de\u011ferin nas\u0131l yorumlanaca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r. \u00d6rnek: Excel’de Regresyon \u00c7\u0131kt\u0131s\u0131n\u0131 Yorumlama Diyelim ki, ders \u00e7al\u0131\u015fmak i\u00e7in harcanan saatlerin ve al\u0131nan haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131n say\u0131s\u0131n\u0131n, bir \u00f6\u011frencinin belirli bir \u00fcniversiteye giri\u015f s\u0131nav\u0131nda ald\u0131\u011f\u0131 notu etkileyip etkilemedi\u011fini bilmek istiyoruz. Bu […]<\/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-1899","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek: Excel’de Regresyon \u00c7\u0131kt\u0131s\u0131n\u0131 Yorumlama<\/strong><\/span><\/h3>\n
![\"Excel'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/multipleregexcel4.png\")
<\/p>\n Tahmini regresyon denklemi nas\u0131l yaz\u0131l\u0131r<\/strong><\/span><\/h3>\n
Ek kaynaklar<\/strong><\/span><\/h3>\n
\u00c7oklu Do\u011frusal Regresyona Giri\u015f<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"