Bu e\u011fitimde SAS’ta \u00e7oklu do\u011frusal regresyonun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n \u00d6\u011frencilerin final s\u0131nav\u0131 notunu tahmin etmek i\u00e7in ders \u00e7al\u0131\u015fmak i\u00e7in harcanan saat say\u0131s\u0131n\u0131 ve al\u0131nan uygulama s\u0131navlar\u0131n\u0131n say\u0131s\u0131n\u0131 kullanan \u00e7oklu do\u011frusal regresyon modelini uyarlamak istedi\u011fimizi varsayal\u0131m:<\/span><\/p>\n S\u0131nav puan\u0131 = \u03b2 0<\/sub> + \u03b2 1<\/sub> (saat) + \u03b2 2<\/sub> (haz\u0131rl\u0131k s\u0131navlar\u0131)<\/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, verilere \u00e7oklu do\u011frusal regresyon modeli uydurmak i\u00e7in proc reg’i<\/strong> kullanaca\u011f\u0131z:<\/span> <\/p>\n Her tablodaki en alakal\u0131 say\u0131lar\u0131 nas\u0131l yorumlayaca\u011f\u0131n\u0131z a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Bo\u015fluk analizi tablosu:<\/strong><\/span><\/p>\n Regresyon modelinin genel F de\u011feri<\/a> 23,46’d\u0131r<\/b> ve kar\u015f\u0131l\u0131k gelen p de\u011feri <0,0001’dir<\/strong> .<\/span><\/p>\n Bu p de\u011feri 0,05’ten k\u00fc\u00e7\u00fck oldu\u011fundan regresyon modelinin bir b\u00fct\u00fcn olarak istatistiksel olarak anlaml\u0131 oldu\u011fu sonucuna var\u0131yoruz.<\/span><\/p>\n Model uyum tablosu:<\/strong><\/span><\/p>\n R-Kare<\/strong> de\u011feri bize, \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131 ve girilen haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131n say\u0131s\u0131yla a\u00e7\u0131klanabilecek s\u0131nav puanlar\u0131ndaki de\u011fi\u015fimin y\u00fczdesini verir.<\/span><\/p>\n Genel olarak, bir regresyon modelinin R-kare de\u011feri<\/a> ne kadar b\u00fcy\u00fck olursa, yorday\u0131c\u0131 de\u011fi\u015fkenlerin yan\u0131t de\u011fi\u015fkeninin de\u011ferini tahmin etmede o kadar iyi olur.<\/span><\/p>\n Bu durumda s\u0131nav puanlar\u0131ndaki farkl\u0131l\u0131\u011f\u0131n %73,4’\u00fc<\/strong> \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131 ve girilen haz\u0131rl\u0131k s\u0131nav\u0131 say\u0131s\u0131 ile a\u00e7\u0131klanabilir.<\/span><\/p>\n K\u00f6k MSE<\/strong> de\u011ferinin bilinmesi de faydal\u0131d\u0131r. Bu, g\u00f6zlemlenen de\u011ferler ile regresyon \u00e7izgisi aras\u0131ndaki ortalama mesafeyi temsil eder.<\/span><\/p>\n Bu regresyon modelinde g\u00f6zlenen de\u011ferler regresyon do\u011frusundan ortalama 5,3657<\/strong> birim sapmaktad\u0131r.<\/span><\/p>\n Parametre tahminleri tablosu:<\/strong><\/span><\/p>\n Uygun regresyon denklemini yazmak i\u00e7in bu tablodaki parametre tahmin de\u011ferlerini kullanabiliriz:<\/span><\/p>\n S\u0131nav puan\u0131 = 67.674 + 5.556*(saat) \u2013 0.602*(haz\u0131rl\u0131k_s\u0131navlar\u0131)<\/span><\/p>\n Bu denklemi, bir \u00f6\u011frencinin \u00e7al\u0131\u015fma saati ve girdi\u011fi deneme s\u0131nav\u0131 say\u0131s\u0131na g\u00f6re tahmini s\u0131nav puan\u0131n\u0131 bulmak i\u00e7in kullanabiliriz.<\/span><\/p>\n \u00d6rne\u011fin 3 saat ders \u00e7al\u0131\u015f\u0131p 2 haz\u0131rl\u0131k s\u0131nav\u0131na giren bir \u00f6\u011frencinin s\u0131nav puan\u0131n\u0131n 83,1<\/strong> olmas\u0131 gerekir:<\/span><\/p>\n Tahmini s\u0131nav puan\u0131 = 67,674 + 5,556*(3) \u2013 0,602*(2) = 83,1<\/strong><\/span><\/p>\n Saatlere ili\u015fkin p de\u011feri (<0,0001) 0,05’ten k\u00fc\u00e7\u00fckt\u00fcr, bu da s\u0131nav sonucuyla istatistiksel olarak anlaml\u0131 bir ili\u015fkiye sahip oldu\u011fu anlam\u0131na gelir.<\/span><\/p>\n Ancak haz\u0131rl\u0131k s\u0131navlar\u0131 i\u00e7in p de\u011feri (0,5193) 0,05’ten az de\u011fildir, yani s\u0131nav sonucuyla istatistiksel olarak anlaml\u0131 bir ili\u015fkisi yoktur.<\/span><\/p>\n \u0130statistiksel olarak anlaml\u0131 olmad\u0131klar\u0131 i\u00e7in haz\u0131rl\u0131k s\u0131navlar\u0131n\u0131 modelden \u00e7\u0131karmaya ve bunun yerine tek yorday\u0131c\u0131 de\u011fi\u015fken olarak \u00e7al\u0131\u015f\u0131lan saatleri kullanarak basit bir do\u011frusal regresyon<\/a> ger\u00e7ekle\u015ftirmeye karar verebiliriz.<\/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 korelasyon nas\u0131l hesaplan\u0131r?<\/a> \u00c7oklu do\u011frusal regresyon, iki veya daha fazla yorday\u0131c\u0131 de\u011fi\u015fken ile bir yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fkiyi anlamak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir. Bu e\u011fitimde SAS’ta \u00e7oklu do\u011frusal regresyonun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r. 1. Ad\u0131m: Verileri olu\u015fturun \u00d6\u011frencilerin final s\u0131nav\u0131 notunu tahmin etmek i\u00e7in ders \u00e7al\u0131\u015fmak i\u00e7in harcanan saat say\u0131s\u0131n\u0131 ve al\u0131nan uygulama s\u0131navlar\u0131n\u0131n say\u0131s\u0131n\u0131 kullanan \u00e7oklu do\u011frusal regresyon […]<\/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-2563","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n 1. Ad\u0131m: Verileri olu\u015fturun<\/strong><\/span><\/h3>\n
\/*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 Ad\u0131m 2: \u00c7oklu Do\u011frusal Regresyon Ger\u00e7ekle\u015ftirin<\/strong><\/span><\/h3>\n
\/*fit multiple linear regression model*\/<\/span>\nproc reg<\/span> data<\/span> =exam_data;\n model<\/span> score = hours prep_exams;\nrun<\/span> ;<\/strong> <\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/multsas1.jpg\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
SAS’ta basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a>
SAS’ta tek y\u00f6nl\u00fc ANOVA nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"