<\/p>\n
Belirli bir regresyon modelinde do\u011frusal hipotezleri test etmek i\u00e7in R’deki araba<\/strong> paketindeki LinearHypothesis()<\/strong> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n Bu \u00f6zel \u00f6rnek, fit<\/strong> ad\u0131 verilen modeldeki var1<\/strong> ve var2<\/strong> regresyon katsay\u0131lar\u0131n\u0131n birlikte s\u0131f\u0131ra e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test eder.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n Bir s\u0131n\u0131ftaki 10 \u00f6\u011frencinin ders \u00e7al\u0131\u015fmak i\u00e7in harcanan saat say\u0131s\u0131n\u0131, al\u0131nan uygulama s\u0131navlar\u0131n\u0131n say\u0131s\u0131n\u0131 ve final s\u0131nav puan\u0131n\u0131 g\u00f6steren a\u015fa\u011f\u0131daki R veri \u00e7er\u00e7evesine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n \u015eimdi a\u015fa\u011f\u0131daki \u00e7oklu do\u011frusal regresyon modelini R’ye s\u0131\u011fd\u0131rmak istedi\u011fimizi varsayal\u0131m:<\/span><\/p>\n S\u0131nav puan\u0131 = \u03b2 0<\/sub> + \u03b2 1<\/sub> (saat) + \u03b2 2<\/sub> (pratik s\u0131navlar)<\/span><\/p>\n Bu modeli uyarlamak i\u00e7in lm()<\/a> fonksiyonunu kullanabiliriz:<\/span><\/p>\n \u015eimdi saat<\/strong> katsay\u0131s\u0131n\u0131n ve prac_exams<\/strong> de\u011ferlerinin her ikisinin de s\u0131f\u0131r olup olmad\u0131\u011f\u0131n\u0131 test etmek istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n Bunu yapmak i\u00e7in LinearHypothesis()<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/p>\n Hipotez testi a\u015fa\u011f\u0131daki de\u011ferleri d\u00f6nd\u00fcr\u00fcr:<\/span><\/p>\n Bu \u00f6zel hipotez testi a\u015fa\u011f\u0131daki bo\u015f ve alternatif hipotezleri kullan\u0131r:<\/span><\/p>\n Testin p de\u011feri (0,003553) 0,05’ten k\u00fc\u00e7\u00fck oldu\u011fundan s\u0131f\u0131r hipotezini reddediyoruz.<\/span><\/p>\n Ba\u015fka bir deyi\u015fle, saat<\/strong> ve prac_exams<\/strong> i\u00e7in regresyon katsay\u0131lar\u0131n\u0131n her ikisinin de s\u0131f\u0131ra e\u015fit oldu\u011funu s\u00f6yleyecek yeterli kan\u0131t\u0131m\u0131z yok.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler R’deki do\u011frusal regresyon hakk\u0131nda ek bilgi sa\u011flar:<\/span><\/p>\n R’de regresyon \u00e7\u0131kt\u0131s\u0131 nas\u0131l yorumlan\u0131r<\/a> Belirli bir regresyon modelinde do\u011frusal hipotezleri test etmek i\u00e7in R’deki araba paketindeki LinearHypothesis() i\u015flevini kullanabilirsiniz. Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r: linearHypothesis(fit, c(” var1=0 “, ” var2=0 “)) Bu \u00f6zel \u00f6rnek, fit ad\u0131 verilen modeldeki var1 ve var2 regresyon katsay\u0131lar\u0131n\u0131n birlikte s\u0131f\u0131ra e\u015fit olup olmad\u0131\u011f\u0131n\u0131 test eder. A\u015fa\u011f\u0131daki \u00f6rnekte bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir. […]<\/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-3904","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n linearHypothesis(fit, c(\" var1=0<\/span> \", \" var2=0<\/span> \"))<\/strong><\/pre>\n \u00d6rnek: R’de LinearHypothesis() i\u015flevi nas\u0131l kullan\u0131l\u0131r?<\/strong><\/span><\/h2>\n
#create data frame\n<\/span>df <- data.frame(score=c(77, 79, 84, 85, 88, 99, 95, 90, 92, 94),\n hours=c(1, 1, 2, 3, 2, 4, 4, 2, 3, 3),\n prac_exams=c(2, 4, 4, 2, 4, 5, 4, 3, 2, 1))\n\n#view data frame\n<\/span>df\n\n score hours prac_exams\n1 77 1 2\n2 79 1 4\n3 84 2 4\n4 85 3 2\n5 88 2 4\n6 99 4 5\n7 95 4 4\n8 90 2 3\n9 92 3 2\n10 94 3 1\n<\/strong><\/pre>\n #fit multiple linear regression model\n<\/span>fit <- lm(score ~ hours + prac_exams, data=df)\n\n#view summary of model\n<\/span>summary(fit)\n\nCall:\nlm(formula = score ~ hours + prac_exams, data = df)\n\nResiduals:\n Min 1Q Median 3Q Max \n-5.8366 -2.0875 0.1381 2.0652 4.6381 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 72.7393 3.9455 18.436 3.42e-07 ***\nhours 5.8093 1.1161 5.205 0.00125 ** \nprac_exams 0.3346 0.9369 0.357 0.73150 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.59 on 7 degrees of freedom\nMultiple R-squared: 0.8004, Adjusted R-squared: 0.7434 \nF-statistic: 14.03 on 2 and 7 DF, p-value: 0.003553\n<\/strong><\/pre>\n library<\/span> (car)\n\n#perform hypothesis test for hours=0 and prac_exams=0\n<\/span>linearHypothesis(fit, c(\" hours=0<\/span> \", \" prac_exams=0<\/span> \"))\n\nLinear hypothesis testing\n\nHypothesis:\nhours = 0\nprac_exams = 0\n\nModel 1: restricted model\nModel 2: score ~ hours + prac_exams\n\n Res.Df RSS Df Sum of Sq F Pr(>F) \n1 9 452.10 \n2 7 90.24 2 361.86 14.035 0.003553 **\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n\n
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Ek kaynaklar<\/strong><\/span><\/h2>\n
R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de lojistik regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/p>\n","protected":false},"excerpt":{"rendered":"