{"id":2946,"date":"2023-07-19T23:21:19","date_gmt":"2023-07-19T23:21:19","guid":{"rendered":"https:\/\/statorials.org\/tr\/rde-coklu-regresyonu-tahmin-edin\/"},"modified":"2023-07-19T23:21:19","modified_gmt":"2023-07-19T23:21:19","slug":"rde-coklu-regresyonu-tahmin-edin","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/rde-coklu-regresyonu-tahmin-edin\/","title":{"rendered":"\u00c7oklu regresyon modeli kullan\u0131larak r'deki de\u011ferler nas\u0131l tahmin edilir?"},"content":{"rendered":"

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Tak\u0131lan \u00e7oklu do\u011frusal regresyon modelini kullanarak R’deki de\u011ferleri tahmin etmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullanabilirsiniz:<\/span><\/span><\/p>\n

 #define new observation\n<\/span>new <- data. frame<\/span> (x1=c(5), x2=c(10), x3=c(12.5))\n\n#use fitted model to predict the response value for the new observation\n<\/span>predict(model, newdata=new)<\/strong><\/pre>\n
 A\u015fa\u011f\u0131daki \u00f6rnekte bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n
 \u00d6rnek: Tak\u0131lan \u00e7oklu do\u011frusal regresyon modelini kullanarak de\u011ferleri tahmin etme<\/strong><\/span><\/h3>\n
 R’de basketbol oyuncular\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki veri k\u00fcmesine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (rating=c(67, 75, 79, 85, 90, 96, 97),\n                 points=c(8, 12, 16, 15, 22, 28, 24),\n                 assists=c(4, 6, 6, 5, 3, 8, 7),\n                 rebounds=c(1, 4, 3, 3, 2, 6, 7))\n\n#view data frame\n<\/span>df\n\n  rating points assists rebounds\n1 67 8 4 1\n2 75 12 6 4\n3 79 16 6 3\n4 85 15 5 3\n5 90 22 3 2\n6 96 28 8 6\n7 97 24 7 7\n<\/strong><\/pre>\n
 \u015eimdi tahmin de\u011fi\u015fkenleri olarak say\u0131<\/strong> , asist<\/strong> ve ribaundlar\u0131<\/strong> ve tepki de\u011fi\u015fkeni<\/a> olarak da reytingi<\/strong> kullanarak \u00e7oklu do\u011frusal regresyon modeli uygulad\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n
 #fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ points + assists + rebounds, data = df)\n\nResiduals:\n      1 2 3 4 5 6 7 \n-1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)   \n(Intercept) 66.4355 6.6932 9.926 0.00218 **\npoints 1.2152 0.2788 4.359 0.02232 * \nassists -2.5968 1.6263 -1.597 0.20860   \nrebounds 2.8202 1.6118 1.750 0.17847   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.193 on 3 degrees of freedom\nMultiple R-squared: 0.9589, Adjusted R-squared: 0.9179 \nF-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396\n<\/strong><\/pre>\n
 Tahmin<\/strong> s\u00fctunundaki de\u011ferlerden uygun regresyon modelini yazabiliriz:<\/span><\/p>\n
 Skor = 66.4355 + 1.2151 (say\u0131) \u2013 2.5968 (asist) + 2.8202 (ribaund)<\/span><\/p>\n
 20 say\u0131, 5 asist ve 2 ribaunt yapan yeni bir oyuncunun reytingini tahmin etmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n
 #define new player\n<\/span>new <- data. frame<\/span> (points=c(20), assists=c(5), rebounds=c(2))\n\n#use the fitted model to predict the rating for the new player\n<\/span>predict(model, newdata=new)\n\n       1 \n83.39607 \n<\/strong><\/pre>\n
 Model, bu yeni oyuncunun 83.39607<\/strong> reytinge sahip olaca\u011f\u0131n\u0131 \u00f6ng\u00f6r\u00fcyor.<\/span><\/p>\n
 Yeni oyuncunun de\u011ferlerini uygun regresyon denklemine yerle\u015ftirerek bunun do\u011fru oldu\u011funu do\u011frulayabiliriz:<\/span><\/p>\n