\u00d6rne\u011fin, belirli bir \u00fcniversitedeki \u00f6\u011frencilerin s\u0131nav puanlar\u0131n\u0131<\/em> tahmin etmek i\u00e7in \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131n\u0131<\/em> kullanmak istedi\u011fimizi varsayal\u0131m. A\u015fa\u011f\u0131daki iki regresyon modelini uyarlamaya karar verebiliriz:<\/span><\/p>\n Tam model:<\/strong> puan = \u03b2 0<\/sub> + B 1<\/sub> (saat) + B 2<\/sub> (saat) 2<\/sup><\/span><\/p>\n Azalt\u0131lm\u0131\u015f model:<\/strong> puan = \u03b2 0<\/sub> + B 1<\/sub> (saat)<\/span><\/p>\n A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, tam modelin azalt\u0131lm\u0131\u015f modelden \u00f6nemli \u00f6l\u00e7\u00fcde daha iyi bir uyum sa\u011flay\u0131p sa\u011flamad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in R’de uyum eksikli\u011fi testinin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir.<\/span><\/p>\n \u0130lk olarak, 50 \u00f6\u011frenci i\u00e7in \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131n\u0131 ve kazan\u0131lan s\u0131nav puanlar\u0131n\u0131 i\u00e7eren bir veri k\u00fcmesi olu\u015fturmak i\u00e7in a\u015fa\u011f\u0131daki kodu kullanaca\u011f\u0131z:<\/span><\/p>\n Daha sonra saatlerle puan aras\u0131ndaki ili\u015fkiyi g\u00f6rselle\u015ftirmek i\u00e7in bir da\u011f\u0131l\u0131m grafi\u011fi olu\u015fturaca\u011f\u0131z:<\/span> <\/p>\n Daha sonra veri k\u00fcmesine iki farkl\u0131 regresyon modeli yerle\u015ftirece\u011fiz:<\/span><\/p>\n Daha sonra, iki model aras\u0131nda uyum eksikli\u011fi testi ger\u00e7ekle\u015ftirmek i\u00e7in anova()<\/strong> komutunu kullanaca\u011f\u0131z:<\/span><\/p>\n F testi istatisti\u011fi 10,554<\/strong> ve buna kar\u015f\u0131l\u0131k gelen p de\u011feri 0,002144<\/strong> olarak ortaya \u00e7\u0131k\u0131yor. Bu p de\u011feri 0,05’ten k\u00fc\u00e7\u00fck oldu\u011fundan testin s\u0131f\u0131r hipotezini reddedebilir ve tam modelin indirgenmi\u015f modele g\u00f6re istatistiksel olarak anlaml\u0131 derecede daha iyi bir uyum sa\u011flad\u0131\u011f\u0131 sonucuna varabiliriz.<\/span><\/p>\n Son olarak, nihai modeli (tam model) orijinal veri k\u00fcmesine g\u00f6re g\u00f6rselle\u015ftirebiliriz:<\/span> <\/p>\n Model e\u011frisinin verilere olduk\u00e7a iyi uyum sa\u011flad\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> Tam regresyon modelinin, modelin azalt\u0131lm\u0131\u015f versiyonuna g\u00f6re veri setine \u00f6nemli \u00f6l\u00e7\u00fcde daha iyi uyum sa\u011flay\u0131p sa\u011flamad\u0131\u011f\u0131n\u0131 belirlemek i\u00e7in uyum eksikli\u011fi testi kullan\u0131l\u0131r. \u00d6rne\u011fin, belirli bir \u00fcniversitedeki \u00f6\u011frencilerin s\u0131nav puanlar\u0131n\u0131 tahmin etmek i\u00e7in \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131n\u0131 kullanmak istedi\u011fimizi varsayal\u0131m. A\u015fa\u011f\u0131daki iki regresyon modelini uyarlamaya karar verebiliriz: Tam model: puan = \u03b2 0 + B 1 (saat) […]<\/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-1463","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n 1. Ad\u0131m: Veri k\u00fcmesi olu\u015fturun ve g\u00f6rselle\u015ftirin<\/strong><\/span><\/h3>\n
#make this example reproducible\n<\/span>set. seeds<\/span> (1)\n\n#create dataset\n<\/span>df <- data. frame<\/span> (hours = runif<\/span> (50, 5, 15), score=50)\ndf$score = df$score + df$hours^3\/150 + df$hours* runif<\/span> (50, 1, 2)\n\n#view first six rows of data\n<\/span>head(df)\n\n hours score\n1 7.655087 64.30191\n2 8.721239 70.65430\n3 10.728534 73.66114\n4 14.082078 86.14630\n5 7.016819 59.81595\n6 13.983897 83.60510<\/strong><\/pre>\n #load ggplot2 visualization package\n<\/span>library<\/span> (ggplot2)\n\n#create scatterplot\n<\/span>ggplot(df, aes<\/span> (x=hours, y=score)) +\n geom_point()<\/strong> <\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/manquefit1.png\")
<\/p>\n Ad\u0131m 2: \u0130ki farkl\u0131 modeli veri k\u00fcmesine yerle\u015ftirin<\/strong><\/span><\/h3>\n
#fit full model\n<\/span>full <- lm(score ~ poly<\/span> (hours,2), data=df)\n<\/span>\n#fit reduced model\nreduced <- lm(score ~ hours, data=df)<\/span><\/span>\n<\/span><\/strong><\/pre>\n 3. Ad\u0131m: Uyum eksikli\u011fi testi yap\u0131n<\/strong><\/span><\/h3>\n
#lack of fit test\n<\/span>anova(full, reduced)\n\nAnalysis of Variance Table\n\nModel 1: score ~ poly(hours, 2)\nModel 2: score ~ hours\n Res.Df RSS Df Sum of Sq F Pr(>F) \n1 47 368.48 \n2 48 451.22 -1 -82.744 10.554 0.002144 **\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n 4. Ad\u0131m: Son modeli g\u00f6rselle\u015ftirin<\/strong><\/span><\/h3>\n
ggplot(df, aes<\/span> (x=hours, y=score)) + \n geom_point() +\n stat_smooth(method=' lm<\/span> ', formula = y ~ poly<\/span> (x,2), size = 1) + \n xlab(' Hours Studied<\/span> ') +\n ylab(' Score<\/span> ')<\/strong><\/span> <\/pre>\n![\"R'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/manquefit2.png\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de polinom regresyonu nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"