A\u015fa\u011f\u0131daki \u015fekilde hesaplan\u0131r:<\/span><\/p>\n AIC = 2K \u2013 2 ln<\/em> (L)<\/span><\/p>\n Alt\u0131n:<\/span><\/p>\n AIC, verilerdeki en fazla varyasyonu a\u00e7\u0131klayan modeli bulmak ve a\u015f\u0131r\u0131 say\u0131da parametre kullanan modelleri cezaland\u0131rmak i\u00e7in tasarlanm\u0131\u015ft\u0131r.<\/span><\/p>\n Birden fazla regresyon modelini yerle\u015ftirdikten sonra<\/span> her modelin AIC de\u011ferini kar\u015f\u0131la\u015ft\u0131rabilirsiniz. AIC ne kadar d\u00fc\u015f\u00fckse model o kadar uygundur.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, SAS’taki farkl\u0131 regresyon modelleri i\u00e7in AIC’nin nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n \u00d6\u011frencilerin bir s\u0131n\u0131fta alaca\u011f\u0131 s\u0131nav notunu tahmin etmek i\u00e7in \u00fc\u00e7 farkl\u0131 \u00e7oklu do\u011frusal regresyon modelini<\/a> e\u015fle\u015ftirmek istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n Her modelde kullanaca\u011f\u0131m\u0131z tahmin de\u011fi\u015fkenleri \u015funlard\u0131r:<\/span><\/p>\n \u00d6ncelikle 20 \u00f6\u011frenciye y\u00f6nelik bu bilgileri i\u00e7eren bir veri seti olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki kodu kullanaca\u011f\u0131z:<\/span><\/p>\n Daha sonra, bu regresyon modellerinin her birine uyacak \u015fekilde proc reg<\/strong> kullanaca\u011f\u0131z ve her model i\u00e7in AIC de\u011ferlerini hesaplamak amac\u0131ylaselection =adjrsq sse aic<\/strong> ifadesini kullanaca\u011f\u0131z:<\/span> <\/p>\n Sonu\u00e7tan her model i\u00e7in AIC de\u011ferlerini g\u00f6rebiliriz:<\/span><\/p>\n En d\u00fc\u015f\u00fck AIC de\u011ferine sahip model, yorday\u0131c\u0131 de\u011fi\u015fken olarak yaln\u0131zca saatleri i\u00e7eren modeldir.<\/span><\/p>\n Bu nedenle a\u015fa\u011f\u0131daki modelin verilere en iyi \u015fekilde uydu\u011funu beyan ederiz:<\/span><\/p>\n Puan = \u03b2 0<\/sub> + \u03b2 1<\/sub> (\u00e7al\u0131\u015f\u0131lan saat)<\/span><\/p>\n Bu modeli en iyisi olarak belirledikten sonra, onu yerle\u015ftirebilir ve \u00e7al\u0131\u015f\u0131lan saat ile \u00f6\u011frenci notu aras\u0131ndaki kesin ili\u015fkiyi belirlemek i\u00e7in R-kare de\u011feri ve beta katsay\u0131lar\u0131 da dahil olmak \u00fczere sonu\u00e7lar\u0131 analiz edebiliriz. ‘Final S\u0131nav\u0131.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde SAS’ta di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n SAS’ta basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a> Akaike Bilgi Kriteri (AIC), \u00e7oklu regresyon modellerinin uyumunu kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in kullan\u0131lan bir \u00f6l\u00e7\u00fcmd\u00fcr. A\u015fa\u011f\u0131daki \u015fekilde hesaplan\u0131r: AIC = 2K \u2013 2 ln (L) Alt\u0131n: K: Model parametrelerinin say\u0131s\u0131. K’nin varsay\u0131lan de\u011feri 2’dir, dolay\u0131s\u0131yla yaln\u0131zca bir tahmin de\u011fi\u015fkeni olan bir modelin K de\u011feri 2+1 = 3 olacakt\u0131r. ln (L) : Modelin log-olas\u0131l\u0131\u011f\u0131. \u00c7o\u011fu istatistik yaz\u0131l\u0131m\u0131 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-4434","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n\n
\u00d6rnek: SAS’ta AIC nas\u0131l hesaplan\u0131r?<\/strong><\/span><\/h2>\n
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\/*create dataset*\/\n<\/span>data<\/span> exam_data;\n input<\/span> hours prep_exams score;\n datalines<\/span> ;\n1 1 76\n2 3 78\n2 3 85\n4 5 88\n2 2 72\n1 2 69\n5 1 94\n4 1 94\n2 0 88\n4 3 92\n4 4 90\n3 3 75\n6 2 96\n5 4 90\n3 4 82\n4 4 85\n6 5 99\n2 1 83\n1 0 62\n2 1 76\n;\nrun<\/span> ;\n<\/strong><\/pre>\n \/*fit multiple linear regression models and calculate AIC for each model*\/<\/span>\nproc reg<\/span> data<\/span> =exam_data;\n model<\/span> score = hours prep_exams \/ selection=adjrsq sse aic;\nrun<\/span> ;<\/strong> <\/pre>\n![\"SAS'ta](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/aic1.png\")
<\/p>\n\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
SAS’ta \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a>
SAS’ta R-kare nas\u0131l hesaplan\u0131r<\/a>
SAS’ta RMSE nas\u0131l hesaplan\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"