\u00d6rne\u011fin, a\u015fa\u011f\u0131daki grafik logaritmik bozunman\u0131n bir \u00f6rne\u011fini g\u00f6stermektedir:<\/span> <\/p>\n Bu t\u00fcr bir durum i\u00e7in, yorday\u0131c\u0131 de\u011fi\u015fken ile yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fki, logaritmik regresyon kullan\u0131larak iyi bir \u015fekilde modellenebilir.<\/span><\/p>\n Logaritmik regresyon modelinin denklemi a\u015fa\u011f\u0131daki formu al\u0131r:<\/span><\/p>\n y = a + b*ln(x)<\/strong><\/span><\/p>\n Alt\u0131n:<\/span><\/p>\n A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, R’de logaritmik regresyonun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir.<\/span><\/p>\n \u00d6ncelikle iki de\u011fi\u015fken i\u00e7in sahte veriler olu\u015ftural\u0131m: x<\/em> ve y<\/em> :<\/span><\/p>\n \u015eimdi x<\/em> ile y<\/em> aras\u0131ndaki ili\u015fkiyi g\u00f6rselle\u015ftirmek i\u00e7in h\u0131zl\u0131 bir da\u011f\u0131l\u0131m grafi\u011fi olu\u015ftural\u0131m:<\/span> <\/p>\n Grafikten iki de\u011fi\u015fken aras\u0131nda net bir logaritmik bozunma modelinin oldu\u011funu g\u00f6rebiliriz. Yan\u0131t de\u011fi\u015fkeninin de\u011feri, y<\/em> , ilk ba\u015fta h\u0131zl\u0131 bir \u015fekilde azal\u0131r, daha sonra zamanla yava\u015flar.<\/span><\/p>\n Bu nedenle de\u011fi\u015fkenler aras\u0131ndaki ili\u015fkiyi tan\u0131mlamak i\u00e7in logaritmik bir regresyon denklemi kullanmak ak\u0131ll\u0131ca g\u00f6r\u00fcnmektedir.<\/span><\/p>\n Daha sonra, tahmin de\u011fi\u015fkeni olarak x’in<\/em> ve yan\u0131t de\u011fi\u015fkeni olarak y’nin<\/em> do\u011fal logaritmas\u0131n\u0131 kullanarak logaritmik regresyon modeline uyum sa\u011flamak i\u00e7in lm()<\/strong> i\u015flevini kullanaca\u011f\u0131z.<\/span><\/p>\n Modelin genel F de\u011feri<\/a> 828,2’dir ve buna kar\u015f\u0131l\u0131k gelen p de\u011feri son derece d\u00fc\u015f\u00fckt\u00fcr (3,702e-13), bu da modelin bir b\u00fct\u00fcn olarak faydal\u0131 oldu\u011funu g\u00f6stermektedir.<\/span><\/p>\n \u00c7\u0131kt\u0131 tablosundaki katsay\u0131lar\u0131 kullanarak uygun logaritmik regresyon denkleminin \u015f\u00f6yle oldu\u011funu g\u00f6rebiliriz:<\/span><\/p>\n y = 63.0686 \u2013 20.1987 * ln(x)<\/strong><\/span><\/p>\n Bu denklemi, tahmin de\u011fi\u015fkeni x’in<\/em> de\u011ferine dayanarak yan\u0131t de\u011fi\u015fkeni y’yi<\/em> tahmin etmek i\u00e7in kullanabiliriz. \u00d6rne\u011fin, x<\/em> = 12 ise y’nin<\/em> 12,87<\/strong> olaca\u011f\u0131n\u0131 tahmin ederiz:<\/span><\/p>\n y = 63,0686 \u2013 20,1987 * ln(12) = 12,87<\/strong><\/span><\/p>\n Bonus:<\/strong> Belirli bir tahminci ve yan\u0131t de\u011fi\u015fkeni i\u00e7in logaritmik regresyon denklemini otomatik olarak hesaplamak i\u00e7in bu \u00e7evrimi\u00e7i logaritmik regresyon hesaplay\u0131c\u0131s\u0131n\u0131 kullanmaktan \u00e7ekinmeyin.<\/span><\/p>\n Son olarak logaritmik regresyon modelinin verilere ne kadar iyi uydu\u011funu g\u00f6rselle\u015ftirmek i\u00e7in h\u0131zl\u0131 bir grafik olu\u015fturabiliriz:<\/span><\/span> <\/p>\n Logaritmik regresyon modelinin bu \u00f6zel veri setine uyma konusunda iyi bir i\u015f \u00e7\u0131kard\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> Logaritmik regresyon, b\u00fcy\u00fcmenin veya d\u00fc\u015f\u00fc\u015f\u00fcn ilk ba\u015fta h\u0131zl\u0131 bir \u015fekilde h\u0131zland\u0131\u011f\u0131 ve daha sonra zamanla yava\u015flad\u0131\u011f\u0131 durumlar\u0131 modellemek i\u00e7in kullan\u0131lan bir regresyon t\u00fcr\u00fcd\u00fcr. \u00d6rne\u011fin, a\u015fa\u011f\u0131daki grafik logaritmik bozunman\u0131n bir \u00f6rne\u011fini g\u00f6stermektedir: Bu t\u00fcr bir durum i\u00e7in, yorday\u0131c\u0131 de\u011fi\u015fken ile yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fki, logaritmik regresyon kullan\u0131larak iyi bir \u015fekilde modellenebilir. Logaritmik regresyon modelinin denklemi a\u015fa\u011f\u0131daki […]<\/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-1452","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/expregexcel2.png\")
<\/p>\n\n
1. Ad\u0131m: Verileri olu\u015fturun<\/strong><\/span><\/h3>\n
x=1:15\n\ny=c(59, 50, 44, 38, 33, 28, 23, 20, 17, 15, 13, 12, 11, 10, 9.5)\n<\/strong><\/pre>\n
2. Ad\u0131m: Verileri g\u00f6rselle\u015ftirin<\/strong><\/h3>\n
plot(x, y)<\/strong> <\/pre>\n
![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/logregr1.png\")
<\/p>\n Ad\u0131m 3: Logaritmik regresyon modelini yerle\u015ftirin<\/strong><\/span><\/h3>\n
#fit the model<\/span>\nmodel <- lm(y ~ log<\/span> (x))<\/strong>\n\n#view the output of the model<\/span>\nsummary(model)\n\nCall:\nlm(formula = y ~ log(x))\n\nResiduals:\n Min 1Q Median 3Q Max \n-4.069 -1.313 -0.260 1.127 3.122 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 63.0686 1.4090 44.76 1.25e-15 ***\nlog(x) -20.1987 0.7019 -28.78 3.70e-13 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.054 on 13 degrees of freedom\nMultiple R-squared: 0.9845, Adjusted R-squared: 0.9834 \nF-statistic: 828.2 on 1 and 13 DF, p-value: 3.702e-13<\/strong><\/pre>\n Ad\u0131m 4: Logaritmik regresyon modelini g\u00f6rselle\u015ftirin<\/strong><\/span><\/h3>\n
#plot x vs. y<\/span>\nplot(x, y)\n\n#define x-values to use for regression line\n<\/span>x=seq(from= 1<\/span> , to= 15<\/span> , length. out<\/span> = 1000<\/span> )\n\n#use the model to predict the y-values based on the x-values\n<\/span>y=predict(model,newdata=list(x=seq(from= 1<\/span> ,to= 15<\/span> ,length. out<\/span> = 1000<\/span> )),\n interval=\" confidence<\/span> \")\n\n#add the fitted regression line to the plot (lwd specifies the width of the line)\n<\/span>matlines(x,y, lwd= 2<\/span> )\n<\/strong><\/pre>\n![\"R'de](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/logregr2.png\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de ikinci dereceden regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a>
R’de \u00fcstel regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de polinom regresyonu nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"