{"id":496,"date":"2023-07-29T17:26:06","date_gmt":"2023-07-29T17:26:06","guid":{"rendered":"https:\/\/statorials.org\/tr\/balik-regresyonu\/"},"modified":"2023-07-29T17:26:06","modified_gmt":"2023-07-29T17:26:06","slug":"balik-regresyonu","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/balik-regresyonu\/","title":{"rendered":"Say\u0131m verileri i\u00e7in poisson regresyonuna yumu\u015fak bir giri\u015f"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Regresyon,<\/strong> bir veya daha fazla yorday\u0131c\u0131 de\u011fi\u015fken ile bir <a href=\"https:\/\/statorials.org\/tr\/degiskenleri-aciklayici-yanitlar\/\" target=\"_blank\" rel=\"noopener\">yan\u0131t de\u011fi\u015fkeni<\/a> aras\u0131ndaki ili\u015fkiyi belirlemek i\u00e7in kullan\u0131labilen istatistiksel bir y\u00f6ntemdir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Poisson regresyonu,<\/strong> yan\u0131t de\u011fi\u015fkeninin &#8220;say\u0131m verileri&#8221; oldu\u011fu \u00f6zel bir regresyon t\u00fcr\u00fcd\u00fcr. A\u015fa\u011f\u0131daki \u00f6rnekler Poisson regresyonunun kullan\u0131labilece\u011fi durumlar\u0131 g\u00f6stermektedir:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u00d6rnek 1:<\/strong> Poisson regresyonu, belirli bir \u00fcniversite program\u0131ndan mezun olan \u00f6\u011frenci say\u0131s\u0131n\u0131, programa girdiklerindeki genel not ortalamalar\u0131na ve cinsiyetlerine g\u00f6re incelemek i\u00e7in kullan\u0131labilir. Bu durumda \u201cmezun olan \u00f6\u011frenci say\u0131s\u0131\u201d yan\u0131t de\u011fi\u015fkeni, \u201cprograma giri\u015f not ortalamas\u0131\u201d s\u00fcrekli yorday\u0131c\u0131 de\u011fi\u015fken ve \u201ccinsiyet\u201d ise kategorik yorday\u0131c\u0131 de\u011fi\u015fkendir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u00d6rnek 2:<\/strong> Poisson regresyonu, hava ko\u015fullar\u0131na (\u201cg\u00fcne\u015fli\u201d, \u201cbulutlu\u201d, \u201cya\u011fmurlu\u201d) ve \u015fehirde \u00f6zel bir olay\u0131n ger\u00e7ekle\u015fip ger\u00e7ekle\u015fmedi\u011fine (\u201cEvet) ba\u011fl\u0131 olarak belirli bir kav\u015faktaki trafik kazas\u0131 say\u0131s\u0131n\u0131 incelemek i\u00e7in kullan\u0131labilir. ya da hay\u0131r&#8221;). Bu durumda, &#8220;yol kazas\u0131 say\u0131s\u0131&#8221; yan\u0131t de\u011fi\u015fkeni iken, &#8220;hava ko\u015fullar\u0131&#8221; ve &#8220;\u00f6zel olay&#8221; kategorik yorday\u0131c\u0131 de\u011fi\u015fkenlerdir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u00d6rnek 3:<\/strong> Poisson regresyonu, g\u00fcn\u00fcn saatine, haftan\u0131n g\u00fcn\u00fcne ve bir sat\u0131\u015f\u0131n ger\u00e7ekle\u015fip ger\u00e7ekle\u015fmedi\u011fine (&#8220;Evet veya hay\u0131r) dayal\u0131 olarak bir ma\u011fazada s\u0131rada bekleyen ki\u015fi say\u0131s\u0131n\u0131 incelemek i\u00e7in kullan\u0131labilir.&#8221; .&#8221;). Bu durumda, &#8220;s\u0131radaki \u00f6n\u00fcn\u00fczdeki ki\u015fi say\u0131s\u0131&#8221; yan\u0131t de\u011fi\u015fkenidir, &#8220;g\u00fcn\u00fcn saati&#8221; ve &#8220;haftan\u0131n g\u00fcn\u00fc&#8221; her ikisi de s\u00fcrekli yorday\u0131c\u0131 de\u011fi\u015fkenlerdir ve &#8220;sat\u0131\u015f devam ediyor&#8221; kategorik yorday\u0131c\u0131 de\u011fi\u015fkendir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u00d6rnek 4:<\/strong> Poisson regresyonu, hava ko\u015fullar\u0131na (\u201cg\u00fcne\u015fli\u201d, \u201cbulutlu\u201d, \u201cya\u011fmurlu\u201d) ve parkur zorlu\u011funa (\u201ckolay\u201d, \u201cya\u011fmurlu\u201d) dayal\u0131 olarak bir triatlonu tamamlayan ki\u015fi say\u0131s\u0131n\u0131 incelemek i\u00e7in kullan\u0131labilir. orta\u201d, \u201czor\u201d). Bu durumda, &#8220;bitiren ki\u015fi say\u0131s\u0131&#8221; yan\u0131t de\u011fi\u015fkeni iken, &#8220;hava ko\u015fullar\u0131&#8221; ve &#8220;parkurun zorlu\u011fu&#8221; kategorik yorday\u0131c\u0131 de\u011fi\u015fkenlerdir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Poisson regresyonunun ger\u00e7ekle\u015ftirilmesi, hangi yorday\u0131c\u0131 de\u011fi\u015fkenlerin (varsa) yan\u0131t de\u011fi\u015fkeni \u00fczerinde istatistiksel olarak anlaml\u0131 bir etkiye sahip oldu\u011funu g\u00f6rmenize olanak tan\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">S\u00fcrekli yorday\u0131c\u0131 de\u011fi\u015fkenler i\u00e7in, o de\u011fi\u015fkendeki bir birimlik art\u0131\u015f\u0131n veya azal\u0131\u015f\u0131n, yan\u0131t de\u011fi\u015fkeninin say\u0131lar\u0131ndaki y\u00fczde de\u011fi\u015fimle nas\u0131l ili\u015fkili oldu\u011funu yorumlayabileceksiniz (\u00f6rne\u011fin, &#8220;GPA&#8217;daki her bir birimlik ek puan art\u0131\u015f\u0131, yan\u0131t de\u011fi\u015fkeninde %12,5&#8217;lik bir art\u0131\u015f).<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kategorik yorday\u0131c\u0131 de\u011fi\u015fkenler i\u00e7in, bir grubun say\u0131mlar\u0131ndaki (\u00f6rne\u011fin, g\u00fcne\u015fli bir g\u00fcnde bir triatlonu tamamlayan ki\u015fi say\u0131s\u0131) di\u011fer bir gruba (\u00f6rne\u011fin, bir triatlonu tamamlayan ki\u015fi say\u0131s\u0131) k\u0131yasla y\u00fczde de\u011fi\u015fimini yorumlayabileceksiniz. ya\u011fmurlu havalarda triatlon).<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Poisson regresyonunun varsay\u0131mlar\u0131<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Poisson regresyonunu ger\u00e7ekle\u015ftirmeden \u00f6nce, Poisson regresyon sonu\u00e7lar\u0131m\u0131z\u0131n ge\u00e7erli olmas\u0131 i\u00e7in a\u015fa\u011f\u0131daki varsay\u0131mlar\u0131n kar\u015f\u0131land\u0131\u011f\u0131ndan emin olmal\u0131y\u0131z:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Varsay\u0131m 1:<\/strong> <strong>Yan\u0131t de\u011fi\u015fkeni say\u0131m verileridir.<\/strong> Geleneksel do\u011frusal regresyonda yan\u0131t de\u011fi\u015fkeni s\u00fcrekli verilerdir. Ancak Poisson regresyonunu kullanabilmek i\u00e7in yan\u0131t de\u011fi\u015fkenimizin 0 veya daha b\u00fcy\u00fck tamsay\u0131lar (\u00f6rne\u011fin 0, 1, 2, 14, 34, 49, 200 vb.) i\u00e7eren say\u0131m verilerinden olu\u015fmas\u0131 gerekir. Yan\u0131t de\u011fi\u015fkenimiz negatif de\u011ferler i\u00e7eremez.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Hipotez 2: G\u00f6zlemler ba\u011f\u0131ms\u0131zd\u0131r.<\/strong> Veri setindeki her <a href=\"https:\/\/statorials.org\/tr\/istatistikte-gozlem\/\" target=\"_blank\" rel=\"noopener\">g\u00f6zlem<\/a> birbirinden ba\u011f\u0131ms\u0131z olmal\u0131d\u0131r. Bu, bir g\u00f6zlemin ba\u015fka bir g\u00f6zlem hakk\u0131nda bilgi sa\u011flayamayaca\u011f\u0131 anlam\u0131na gelir.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Hipotez 3: Hesaplar\u0131n da\u011f\u0131l\u0131m\u0131 Poisson da\u011f\u0131l\u0131m\u0131n\u0131 takip etmektedir.<\/strong> Sonu\u00e7 olarak g\u00f6zlemlenen ve beklenen say\u0131mlar\u0131n benzer olmas\u0131 gerekir. Bunu test etmenin basit bir yolu, beklenen ve g\u00f6zlemlenen say\u0131lar\u0131n grafi\u011fini \u00e7\u0131karmak ve benzer olup olmad\u0131klar\u0131na bakmakt\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Varsay\u0131m 4: Modelin ortalamas\u0131 ve varyans\u0131 e\u015fittir.<\/strong> Bu, say\u0131m da\u011f\u0131l\u0131m\u0131n\u0131n Poisson da\u011f\u0131l\u0131m\u0131n\u0131 takip etti\u011fi varsay\u0131m\u0131ndan kaynaklanmaktad\u0131r. Poisson da\u011f\u0131l\u0131m\u0131 i\u00e7in varyans ortalamayla ayn\u0131 de\u011fere sahiptir. Bu varsay\u0131m kar\u015f\u0131lan\u0131rsa, o zaman <strong>e\u015fit da\u011f\u0131l\u0131ma<\/strong> sahip olursunuz. Ancak a\u015f\u0131r\u0131 da\u011f\u0131l\u0131m yayg\u0131n bir sorun oldu\u011fundan bu varsay\u0131m s\u0131kl\u0131kla ihlal edilir.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\u00d6rnek: R&#8217;de Poisson regresyonu<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\u015eimdi R&#8217;de Poisson regresyonunun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fine ili\u015fkin bir \u00f6rne\u011fi inceleyece\u011fiz.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Arka plan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Belirli bir il\u00e7edeki bir lise beyzbol oyuncusunun okul b\u00f6l\u00fcm\u00fcne (&#8220;A&#8221;, &#8220;B&#8221; veya &#8220;C&#8221;) ve okul notuna ba\u011fl\u0131 olarak ka\u00e7 burs ald\u0131\u011f\u0131n\u0131 bilmek istedi\u011fimizi varsayal\u0131m. \u00fcniversiteye giri\u015f s\u0131nav\u0131 (0 ile 100 aras\u0131nda \u00f6l\u00e7\u00fcl\u00fcr). ).<\/span><\/p>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki kod, 100 beyzbol oyuncusuna ili\u015fkin verileri i\u00e7eren, \u00fczerinde \u00e7al\u0131\u015faca\u011f\u0131m\u0131z veri k\u00fcmesini olu\u015fturur:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(1)\n\n<span style=\"color: #008080;\">#create dataset\n<\/span>data &lt;- data.frame(offers = c(rep(0, 50), rep(1, 30), rep(2, 10), rep(3, 7), rep(4, 3)),\n                   division = sample(c(\"A\", \"B\", \"C\"), 100, replace = TRUE),\n                   exam = c(runif(50, 60, 80), runif(30, 65, 95), runif(20, 75, 95)))<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Verileri anlama<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Poisson regresyon modelini bu veri k\u00fcmesine ger\u00e7ekten yerle\u015ftirmeden \u00f6nce, veri k\u00fcmesinin ilk birka\u00e7 sat\u0131r\u0131n\u0131 g\u00f6rselle\u015ftirerek ve \u00f6zet istatistikleri \u00e7al\u0131\u015ft\u0131rmak i\u00e7in <strong><a href=\"https:\/\/dplyr.tidyverse.org\/\" target=\"_blank\" rel=\"noopener\">dplyr<\/a><\/strong> kitapl\u0131\u011f\u0131n\u0131 kullanarak verileri daha iyi anlayabiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view dimensions of dataset<\/span>\ndim(data)\n\n#[1] 100 3\n\n<span style=\"color: #008080;\">#view first six lines of dataset<\/span>\nhead(data)\n\n# offers division exam\n#1 0 A 73.09448\n#2 0 B 67.06395\n#3 0 B 65.40520\n#4 0 C 79.85368\n#5 0 A 72.66987\n#6 0 C 64.26416\n\n<span style=\"color: #008080;\">#view summary of each variable in dataset<\/span>\nsummary(data)\n\n# offers division exam      \n# Min. :0.00 To:27 Min. :60.26  \n# 1st Qu.:0.00 B:38 1st Qu.:69.86  \n# Median: 0.50 C:35 Median: 75.08  \n# Mean:0.83 Mean:76.43  \n# 3rd Qu.:1.00 3rd Qu.:82.87  \n# Max. :4.00 Max. :93.87  \n\n<span style=\"color: #008080;\">#view mean exam score by number of offers<\/span>\nlibrary(dplyr)\ndata %&gt;%\n  <span style=\"color: #800080;\">group_by<\/span> (offers) %&gt;%\n  <span style=\"color: #800080;\">summarize<\/span> (mean_exam = mean(exam))\n\n# A tibble: 5 x 2\n# offers mean_exam\n#        \n#1 0 70.0\n#2 1 80.8\n#3 2 86.8\n#4 3 83.9\n#5 4 87.9<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Yukar\u0131daki sonu\u00e7tan \u015funlar\u0131 g\u00f6zlemleyebiliriz:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Veri setinde 100 sat\u0131r ve 3 s\u00fctun bulunmaktad\u0131r<\/span><\/li>\n<li> <span style=\"color: #000000;\">Bir oyuncunun ald\u0131\u011f\u0131 minimum teklif say\u0131s\u0131 s\u0131f\u0131r, maksimum d\u00f6rt ve ortalama 0,83 oldu.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Bu veri setinde \u201cA\u201d grubundan 27, \u201cB\u201d grubundan 38 ve \u201cC\u201d grubundan 35 oyuncu yer al\u0131yor.<\/span><\/li>\n<li> <span style=\"color: #000000;\">S\u0131navdan al\u0131nacak minimum puan 60,26, maksimum puan 93,87, ortalama ise 76,43 oldu.<\/span><\/li>\n<li> <span style=\"color: #000000;\">Genel olarak, daha fazla burs teklifi alan oyuncular\u0131n s\u0131nav puanlar\u0131 daha y\u00fcksek olma e\u011filimindedir (\u00f6rne\u011fin, hi\u00e7bir teklif almayan oyuncular\u0131n ortalama s\u0131nav puan\u0131 70,0 ve 4 teklif alan oyuncular\u0131n ortalama inceleme puan\u0131 87,9&#8217;dur).<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Ayr\u0131ca oyuncular taraf\u0131ndan al\u0131nan tekliflerin say\u0131s\u0131n\u0131 b\u00f6l\u00fcme g\u00f6re g\u00f6rselle\u015ftirmek i\u00e7in bir histogram da olu\u015fturabiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>ggplot2<\/em> package<\/span>\nlibrary(ggplot2)\n\n<span style=\"color: #008080;\">#create histogram\n<\/span>ggplot(data, aes(offers, fill = division)) +\n  geom_histogram(binwidth=.5, position=\"dodge\")<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u00c7o\u011fu oyuncunun ya hi\u00e7 teklif almad\u0131\u011f\u0131n\u0131 ya da sadece bir teklif ald\u0131\u011f\u0131n\u0131 g\u00f6r\u00fcyoruz. Bu, <a href=\"https:\/\/statorials.org\/tr\/balik-dagitimi\/\" target=\"_blank\" rel=\"noopener\">Poisson da\u011f\u0131l\u0131mlar\u0131n\u0131<\/a> izleyen veri k\u00fcmelerinin tipik bir \u00f6rne\u011fidir: Yan\u0131t de\u011ferlerinin b\u00fcy\u00fck bir k\u0131sm\u0131 s\u0131f\u0131rd\u0131r.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Poisson regresyon modelinin uygulanmas\u0131<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Daha sonra <strong>glm()<\/strong> fonksiyonunu kullanarak ve model i\u00e7in <strong>family=&#8221;fish&#8221;<\/strong> kullanmak istedi\u011fimizi belirterek modeli ayarlayabiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit the model\n<\/span>model &lt;- glm(offers ~ division + exam, <span style=\"color: #800080;\">family = \"fish\"<\/span> , data = data)\n\n<span style=\"color: #000000;\">#view model output\n<\/span>summary(model)\n\n#Call:\n#glm(formula = offers ~ division + exam, family = \"fish\", data = data)\n#\n#Deviance Residuals: \n# Min 1Q Median 3Q Max  \n#-1.2562 -0.8467 -0.5657 0.3846 2.5033  \n#\n#Coefficients:\n#Estimate Std. Error z value Pr(&gt;|z|)    \n#(Intercept) -7.90602 1.13597 -6.960 3.41e-12 ***\n#divisionB 0.17566 0.27257 0.644 0.519    \n#divisionC -0.05251 0.27819 -0.189 0.850    \n#exam 0.09548 0.01322 7.221 5.15e-13 ***\n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#\n#(Dispersion parameter for fish family taken to be 1)\n#\n# Null deviance: 138,069 on 99 degrees of freedom\n#Residual deviance: 79,247 on 96 degrees of freedom\n#AIC: 204.12\n#\n#Number of Fisher Scoring iterations: 5\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Sonu\u00e7tan \u015funlar\u0131 g\u00f6zlemleyebiliriz:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Poisson regresyon katsay\u0131lar\u0131, tahminlerin standart hatas\u0131, z puanlar\u0131 ve kar\u015f\u0131l\u0131k gelen p de\u011ferlerinin t\u00fcm\u00fc sa\u011flanmaktad\u0131r.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><em>\u0130nceleme<\/em> katsay\u0131s\u0131 <strong>0,09548&#8217;dir<\/strong> ; bu, <em>incelemedeki<\/em> bir birimlik art\u0131\u015f i\u00e7in teklif say\u0131s\u0131na ili\u015fkin beklenen g\u00fcnl\u00fck say\u0131s\u0131n\u0131n <strong>0,09548<\/strong> oldu\u011funu g\u00f6sterir. Bunu yorumlaman\u0131n daha basit bir yolu \u00fcstel de\u011feri almakt\u0131r, yani <strong>e <sup>0,09548<\/sup><\/strong> = <strong>1,10<\/strong> . Bu, giri\u015f s\u0131nav\u0131nda kazan\u0131lan her ek puan i\u00e7in al\u0131nan teklif say\u0131s\u0131nda %10&#8217;luk bir art\u0131\u015f oldu\u011fu anlam\u0131na gelir.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><em>B Grubunun<\/em> katsay\u0131s\u0131 <strong>0,1756&#8217;d\u0131r<\/strong> , bu da B Grubundaki bir oyuncu i\u00e7in beklenen teklif say\u0131s\u0131n\u0131n A Grubundaki bir oyuncuya g\u00f6re <strong>0,1756<\/strong> daha y\u00fcksek oldu\u011funu g\u00f6sterir. Bunu yorumlaman\u0131n daha basit bir yolu \u00fcsl\u00fc de\u011feri almakt\u0131r, yani <strong>e <sup>0,1756<\/sup><\/strong> = <strong>1.19<\/strong> . Bu, B b\u00f6l\u00fcm\u00fcndeki oyuncular\u0131n A b\u00f6l\u00fcm\u00fcndeki oyunculara g\u00f6re %19 daha fazla teklif ald\u0131\u011f\u0131 anlam\u0131na gelir. Bu fark\u0131n istatistiksel olarak anlaml\u0131 olmad\u0131\u011f\u0131n\u0131 unutmay\u0131n (p = 0,519).<\/span><\/li>\n<li> <span style=\"color: #000000;\"><em>C Grubunun<\/em> katsay\u0131s\u0131 <strong>-0,05251&#8217;dir<\/strong> ; bu, C Grubundaki bir oyuncu i\u00e7in teklif say\u0131s\u0131na ili\u015fkin beklenen g\u00fcnl\u00fck say\u0131s\u0131n\u0131n, A Grubundaki bir oyuncuya g\u00f6re <strong>0,05251<\/strong> <i>daha d\u00fc\u015f\u00fck<\/i> oldu\u011funu g\u00f6sterir. Bunu yorumlaman\u0131n daha basit bir yolu, \u00fcstel de\u011feri almakt\u0131r. yani <strong>e <sup>0,05251<\/sup><\/strong> = <strong>0,94&#8217;t\u00fcr<\/strong> . Bu, C b\u00f6l\u00fcm\u00fcndeki oyuncular\u0131n A b\u00f6l\u00fcm\u00fcndeki oyunculara g\u00f6re %6 daha az teklif ald\u0131\u011f\u0131 anlam\u0131na gelir. Bu fark\u0131n istatistiksel olarak anlaml\u0131 olmad\u0131\u011f\u0131n\u0131 unutmay\u0131n (p = 850).<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Model sapmas\u0131 hakk\u0131nda da bilgi verilmektedir. \u00d6zellikle <strong>96<\/strong> serbestlik derecesi \u00fczerinden <strong>79.247<\/strong> de\u011ferine sahip olan <em>art\u0131k sapma<\/em> ile ilgileniyoruz. Bu say\u0131lar\u0131 kullanarak modelin verilere uyup uymad\u0131\u011f\u0131n\u0131 g\u00f6rmek i\u00e7in ki-kare uyum iyili\u011fi testi yapabiliriz. A\u015fa\u011f\u0131daki kod bu testin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6stermektedir:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong>pchisq(79.24679, 96, lower.tail = FALSE)\n\n#[1] 0.8922676\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Bu testin p de\u011feri <strong>0,89<\/strong> olup, 0,05 anlaml\u0131l\u0131k seviyesinin olduk\u00e7a \u00fczerindedir. Verilerin modele olduk\u00e7a iyi uyum sa\u011flad\u0131\u011f\u0131 sonucuna varabiliriz.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sonu\u00e7lar\u0131 G\u00f6r\u00fcnt\u00fcle<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki kodu kullanarak b\u00f6l\u00fcm ve giri\u015f s\u0131nav\u0131 sonu\u00e7lar\u0131na g\u00f6re beklenen burs teklifi say\u0131s\u0131n\u0131 g\u00f6steren bir grafik de olu\u015fturabiliriz:<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find predicted number of offers using the fitted Poisson regression model\n<\/span>data$phat &lt;- predict(model, type=\"response\")\n\n<span style=\"color: #008080;\">#create plot that shows number of offers based on division and exam score\n<\/span>ggplot(data, aes(x = exam, y = phat, color = division)) +\n  geom_point(aes(y = offers), alpha = .7, position = position_jitter(h = .2)) +\n  geom_line() +\n  labs(x = \"Entrance Exam Score\", y = \"Expected number of scholarship offers\")<\/strong><\/pre>\n<h3><\/h3>\n<p> <span style=\"color: #000000;\">Grafik, giri\u015f s\u0131nav\u0131nda y\u00fcksek puan alan oyuncular i\u00e7in beklenen en y\u00fcksek burs tekliflerini g\u00f6stermektedir. Ek olarak, Klasman B&#8217;deki (ye\u015fil \u00e7izgi) oyuncular\u0131n Klasman A veya Klasman C&#8217;deki oyunculara g\u00f6re genel olarak daha fazla teklif almas\u0131 gerekti\u011fini g\u00f6rebiliriz.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sonu\u00e7lar\u0131 raporla<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Son olarak regresyon sonu\u00e7lar\u0131n\u0131 bulgular\u0131m\u0131z\u0131 \u00f6zetleyecek \u015fekilde raporlayabiliriz:<\/span><\/p>\n<blockquote>\n<p> <span style=\"color: #000000;\">Beyzbol oyuncular\u0131 taraf\u0131ndan b\u00f6l\u00fcm ve giri\u015f s\u0131nav\u0131 puanlar\u0131na g\u00f6re al\u0131nan burs tekliflerinin say\u0131s\u0131n\u0131 tahmin etmek i\u00e7in bir Poisson regresyonu \u00e7al\u0131\u015ft\u0131r\u0131ld\u0131. Giri\u015f s\u0131nav\u0131nda kazan\u0131lan her ek puan i\u00e7in al\u0131nan teklif say\u0131s\u0131 %10 artar ( <em>p&lt;0,0001)<\/em> . B\u00f6l\u00fcnmenin istatistiksel olarak anlaml\u0131 olmad\u0131\u011f\u0131 g\u00f6r\u00fcld\u00fc.<\/span><\/p>\n<\/blockquote>\n<h3> <span style=\"color: #000000;\"><strong>Ek kaynaklar<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/tr\/dogrusal-regresyon-1\/\" target=\"_blank\" rel=\"noopener\">Basit Do\u011frusal Regresyona Giri\u015f<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/coklu-dogrusal-regresyon\/\" target=\"_blank\" rel=\"noopener\">\u00c7oklu Do\u011frusal Regresyona Giri\u015f<\/a><br \/> <a href=\"https:\/\/statorials.org\/tr\/polinom-regresyonu-1\/\" target=\"_blank\" rel=\"noopener\">Polinom Regresyona Giri\u015f<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Regresyon, bir veya daha fazla yorday\u0131c\u0131 de\u011fi\u015fken ile bir yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fkiyi belirlemek i\u00e7in kullan\u0131labilen istatistiksel bir y\u00f6ntemdir. Poisson regresyonu, yan\u0131t de\u011fi\u015fkeninin &#8220;say\u0131m verileri&#8221; oldu\u011fu \u00f6zel bir regresyon t\u00fcr\u00fcd\u00fcr. A\u015fa\u011f\u0131daki \u00f6rnekler Poisson regresyonunun kullan\u0131labilece\u011fi durumlar\u0131 g\u00f6stermektedir: \u00d6rnek 1: Poisson regresyonu, belirli bir \u00fcniversite program\u0131ndan mezun olan \u00f6\u011frenci say\u0131s\u0131n\u0131, programa girdiklerindeki genel not ortalamalar\u0131na ve [&hellip;]<\/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-496","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Say\u0131m verileri i\u00e7in Poisson regresyonuna yumu\u015fak bir giri\u015f - Statorials<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitim, R&#039;deki ad\u0131m ad\u0131m bir \u00f6rnek de dahil olmak \u00fczere say\u0131m verileri i\u00e7in Poisson regresyonuna yumu\u015fak bir giri\u015f sa\u011flar.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/tr\/balik-regresyonu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Say\u0131m verileri i\u00e7in Poisson regresyonuna yumu\u015fak bir giri\u015f - 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