<\/p>\n
Goldfeld-Quandt testi,<\/strong> bir regresyon modelinde heteroskedastisitenin<\/a> mevcut olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in kullan\u0131l\u0131r.<\/span><\/p>\n Heteroskedastisite, bir regresyon modelinde bir yan\u0131t de\u011fi\u015fkeninin<\/a> farkl\u0131 seviyelerindeki art\u0131klar\u0131n<\/a> e\u015fit olmayan da\u011f\u0131l\u0131m\u0131n\u0131 ifade eder.<\/span><\/p>\n De\u011fi\u015fen varyans mevcutsa bu,do\u011frusal regresyonun, art\u0131klar\u0131n yan\u0131t de\u011fi\u015fkeninin her seviyesinde e\u015fit \u015fekilde da\u011f\u0131ld\u0131\u011f\u0131 y\u00f6n\u00fcndeki temel varsay\u0131mlar\u0131ndan<\/a> birini ihlal eder.<\/span><\/p>\n Bu e\u011fitimde, belirli bir regresyon modelinde de\u011fi\u015fen varyans\u0131n mevcut olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in R’de Goldfeld-Quandt testinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fine ili\u015fkin ad\u0131m ad\u0131m bir \u00f6rnek sunulmaktad\u0131r.<\/span><\/p>\n \u0130lk olarak, R’de yerle\u015fik mtcars<\/strong> veri k\u00fcmesini kullanarak \u00e7oklu do\u011frusal regresyon modeli<\/a> olu\u015fturaca\u011f\u0131z:<\/span><\/p>\n Daha sonra, de\u011fi\u015fen varyans\u0131n mevcut olup olmad\u0131\u011f\u0131n\u0131 belirlemek amac\u0131yla Goldfeld-Quandt testini ger\u00e7ekle\u015ftirmek i\u00e7in lmtest<\/strong> paketindeki gqtest()<\/b> fonksiyonunu kullanaca\u011f\u0131z.<\/span><\/p>\n Bu i\u015flev a\u015fa\u011f\u0131daki s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n gqtest(model, s\u0131ralamaya g\u00f6re, veriler, kesir)<\/span><\/strong><\/p>\n Alt\u0131n:<\/span><\/p>\n *Goldfeld-Quandt testi, veri setinin merkezinde yer alan bir dizi g\u00f6zlemi kald\u0131rarak ve ard\u0131ndan art\u0131klar\u0131n da\u011f\u0131l\u0131m\u0131n\u0131n, veri setlerinin her iki taraf\u0131nda yer alan sonu\u00e7ta ortaya \u00e7\u0131kan iki veri setinden farkl\u0131 olup olmad\u0131\u011f\u0131n\u0131 test ederek \u00e7al\u0131\u015f\u0131r. Merkezi g\u00f6zlemler.<\/span><\/p>\n Genellikle toplam g\u00f6zlemlerin yakla\u015f\u0131k %20’sini kald\u0131rmay\u0131 se\u00e7eriz. Bu durumda mtcars’\u0131n toplamda 32 g\u00f6zlemi vard\u0131r, dolay\u0131s\u0131yla merkezi 7 g\u00f6zlemi kald\u0131rmay\u0131 se\u00e7ebiliriz:<\/span><\/p>\n Sonucun nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Goldfeld-Quandt testi a\u015fa\u011f\u0131daki bo\u015f ve alternatif hipotezleri kullan\u0131r:<\/span><\/p>\n P de\u011feri 0,05’ten k\u00fc\u00e7\u00fck olmad\u0131\u011f\u0131ndan s\u0131f\u0131r hipotezini reddedemiyoruz. Regresyon modelinde heteroskedastisitenin mevcut oldu\u011funu iddia edecek yeterli kan\u0131t\u0131m\u0131z yok.<\/span><\/p>\n Goldfeld-Quandt testinin bo\u015f hipotezini reddetmezseniz, bu durumda heteroskedastisite mevcut de\u011fildir ve orijinal regresyonun sonucunu yorumlamaya devam edebilirsiniz.<\/span><\/p>\n Ancak s\u0131f\u0131r hipotezini reddederseniz bu, verilerde heteroskedastisitenin mevcut oldu\u011fu anlam\u0131na gelir. Bu durumda regresyon \u00e7\u0131kt\u0131 tablosunda g\u00f6r\u00fcnt\u00fclenen standart hatalar g\u00fcvenilir olmayabilir.<\/span><\/p>\n Bu sorunu \u00e7\u00f6zmenin birka\u00e7 yayg\u0131n yolu vard\u0131r:<\/span><\/p>\n 1. Yan\u0131t de\u011fi\u015fkenini d\u00f6n\u00fc\u015ft\u00fcr\u00fcn.<\/strong><\/span><\/p>\n Yan\u0131t de\u011fi\u015fkeni \u00fczerinde bir d\u00f6n\u00fc\u015f\u00fcm ger\u00e7ekle\u015ftirmeyi deneyebilirsiniz; \u00f6rne\u011fin yan\u0131t de\u011fi\u015fkeninin g\u00fcnl\u00fc\u011f\u00fcn\u00fc, karek\u00f6k\u00fcn\u00fc veya k\u00fcp k\u00f6k\u00fcn\u00fc<\/a> alabilirsiniz. Genellikle bu, de\u011fi\u015fen varyans\u0131n ortadan kalkmas\u0131na neden olabilir.<\/span><\/p>\n 2. A\u011f\u0131rl\u0131kl\u0131 regresyonu kullan\u0131n.<\/strong><\/span><\/p>\n A\u011f\u0131rl\u0131kl\u0131 regresyon, her veri noktas\u0131na, uydurulan de\u011ferin varyans\u0131na dayal\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.<\/span><\/p>\n Uygun a\u011f\u0131rl\u0131klar kullan\u0131ld\u0131\u011f\u0131nda a\u011f\u0131rl\u0131kl\u0131 regresyon de\u011fi\u015fen varyans problemini ortadan kald\u0131rabilir.<\/span><\/p>\n R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> Goldfeld-Quandt testi, bir regresyon modelinde heteroskedastisitenin mevcut olup olmad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in kullan\u0131l\u0131r. Heteroskedastisite, bir regresyon modelinde bir yan\u0131t de\u011fi\u015fkeninin farkl\u0131 seviyelerindeki art\u0131klar\u0131n e\u015fit olmayan da\u011f\u0131l\u0131m\u0131n\u0131 ifade eder. De\u011fi\u015fen varyans mevcutsa bu,do\u011frusal regresyonun, art\u0131klar\u0131n yan\u0131t de\u011fi\u015fkeninin her seviyesinde e\u015fit \u015fekilde da\u011f\u0131ld\u0131\u011f\u0131 y\u00f6n\u00fcndeki temel varsay\u0131mlar\u0131ndan birini ihlal eder. Bu e\u011fitimde, belirli bir regresyon modelinde de\u011fi\u015fen varyans\u0131n mevcut […]<\/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-1277","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n 1. Ad\u0131m: Bir regresyon modeli olu\u015fturun<\/strong><\/h3>\n
#fit a regression model<\/span>\nmodel <- lm(mpg~disp+hp, data=mtcars)\n\n#view model summary\n<\/span>summary(model)\n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 30.735904 1.331566 23.083 < 2nd-16 ***\navailable -0.030346 0.007405 -4.098 0.000306 ***\nhp -0.024840 0.013385 -1.856 0.073679 . \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.127 on 29 degrees of freedom\nMultiple R-squared: 0.7482, Adjusted R-squared: 0.7309 \nF-statistic: 43.09 on 2 and 29 DF, p-value: 2.062e-09\n<\/strong><\/pre>\n Ad\u0131m 2: Goldfeld-Quandt testini ger\u00e7ekle\u015ftirin<\/strong><\/span><\/h3>\n
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#load lmtest library<\/span>\nlibrary(lmtest)\n\n#perform the Goldfeld Quandt test<\/span>\ngqtest(model, order.by = ~disp+hp, data = mtcars, fraction = 7)\n\n\tGoldfeld-Quandt test\n\ndata: model\nGQ = 1.0316, df1 = 10, df2 = 9, p-value = 0.486\nalternative hypothesis: variance increases from segment 1 to 2\n<\/strong><\/pre>\n\n
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Sonra ne yapaca\u011f\u0131z<\/strong><\/h3>\n
Ek kaynaklar<\/strong><\/span><\/h3>\n
White’\u0131n testi R’de nas\u0131l yap\u0131l\u0131r?<\/a>
R’de Breusch-Pagan testi nas\u0131l yap\u0131l\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"