Tipik olarak do\u011frusal regresyon uygulad\u0131\u011f\u0131m\u0131zda yan\u0131t de\u011fi\u015fkeninin ortalama de\u011ferini tahmin etmek isteriz.<\/span><\/p>\n Bununla birlikte, yan\u0131t de\u011ferinin herhangi bir<\/em> nicelik veya y\u00fczdelik de\u011ferini (70. y\u00fczdelik dilim, 90. y\u00fczdelik dilim, 98. y\u00fczdelik dilim vb.) tahmin etmek i\u00e7in bunun yerine nicelik regresyonu<\/strong> olarak bilinen bir y\u00f6ntemi kullanabiliriz.<\/span><\/p>\n Bu e\u011fitimde, Python’da niceliksel regresyon ger\u00e7ekle\u015ftirmek i\u00e7in bu i\u015flevin nas\u0131l kullan\u0131laca\u011f\u0131na ili\u015fkin ad\u0131m ad\u0131m bir \u00f6rnek sunulmaktad\u0131r.<\/span><\/p>\n \u00d6ncelikle gerekli paketleri ve fonksiyonlar\u0131 y\u00fckleyece\u011fiz:<\/span><\/p>\n Bu \u00f6rnek i\u00e7in bir \u00fcniversitede 100 \u00f6\u011frencinin ders \u00e7al\u0131\u015fma saatlerini ve ald\u0131klar\u0131 s\u0131nav sonu\u00e7lar\u0131n\u0131 i\u00e7eren bir veri seti olu\u015fturaca\u011f\u0131z:<\/span><\/p>\n Daha sonra, yorday\u0131c\u0131 de\u011fi\u015fken olarak \u00e7al\u0131\u015f\u0131lan saatleri ve yan\u0131t de\u011fi\u015fkeni olarak s\u0131nav puanlar\u0131n\u0131 kullanarak niceliksel bir regresyon modeli uygulayaca\u011f\u0131z.<\/span><\/p>\n Bu modeli, \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131na ba\u011fl\u0131 olarak s\u0131nav puanlar\u0131n\u0131n beklenen y\u00fczde 90’l\u0131k dilimini tahmin etmek i\u00e7in kullanaca\u011f\u0131z:<\/span><\/p>\n Sonu\u00e7tan tahmini regresyon denklemini g\u00f6rebiliriz:<\/span><\/p>\n S\u0131nav puan\u0131n\u0131n 90. y\u00fczdelik dilimi = 59,6104 + 2,8495*(saat)<\/span><\/p>\n \u00d6rne\u011fin 8 saat ders \u00e7al\u0131\u015fan t\u00fcm \u00f6\u011frencilerin 90. y\u00fczdelik puan\u0131 82,4 olmal\u0131d\u0131r:<\/span><\/p>\n S\u0131nav puan\u0131n\u0131n 90. y\u00fczdelik dilimi = 59,6104 + 2,8495*(8) = 82,4<\/strong> .<\/span><\/p>\n \u00c7\u0131kt\u0131 ayr\u0131ca tahmin de\u011fi\u015fkeninin kesi\u015fme noktas\u0131 ve s\u00fcreleri i\u00e7in \u00fcst ve alt g\u00fcven s\u0131n\u0131rlar\u0131n\u0131 da g\u00f6r\u00fcnt\u00fcler.<\/span><\/p>\n Grafi\u011fe yerle\u015ftirilmi\u015f niceliksel regresyon denklemi ile bir da\u011f\u0131l\u0131m grafi\u011fi olu\u015fturarak regresyon sonu\u00e7lar\u0131n\u0131 da g\u00f6rselle\u015ftirebiliriz:<\/span> <\/p>\n Basit bir do\u011frusal regresyon \u00e7izgisinin aksine, bu uydurulmu\u015f \u00e7izginin veriler i\u00e7in “en iyi uyum \u00e7izgisini” temsil etmedi\u011fine dikkat edin. Bunun yerine, yorday\u0131c\u0131 de\u011fi\u015fkenin her d\u00fczeyinde tahmini 90. y\u00fczdelik dilimden ge\u00e7er.<\/span><\/p>\n Python’da basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> Do\u011frusal regresyon, bir veya daha fazla yorday\u0131c\u0131 de\u011fi\u015fken ile bir yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fkiyi anlamak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir. Tipik olarak do\u011frusal regresyon uygulad\u0131\u011f\u0131m\u0131zda yan\u0131t de\u011fi\u015fkeninin ortalama de\u011ferini tahmin etmek isteriz. Bununla birlikte, yan\u0131t de\u011ferinin herhangi bir nicelik veya y\u00fczdelik de\u011ferini (70. y\u00fczdelik dilim, 90. y\u00fczdelik dilim, 98. y\u00fczdelik dilim vb.) tahmin etmek i\u00e7in bunun […]<\/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-1326","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Ad\u0131m 1: Gerekli paketleri y\u00fckleyin<\/strong><\/span><\/h3>\n
import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\nimport<\/span> statsmodels. api<\/span> as<\/span> sm\nimport<\/span> statsmodels. formula<\/span> . api<\/span> as<\/span> smf\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n<\/strong><\/pre>\n 2. Ad\u0131m: Verileri olu\u015fturun<\/strong><\/span><\/h3>\n
#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (0)\n\n#create dataset\n<\/span>obs = 100\n\nhours = np. random<\/span> . uniform<\/span> (1, 10, obs)\nscore = 60 + 2*hours + np. random<\/span> . normal<\/span> (loc=0, scale=.45*hours, size=100)\n\ndf = pd. DataFrame<\/span> ({' hours<\/span> ':hours, ' score<\/span> ':score})\n\n#view first five rows\n<\/span>df. head<\/span> ()\n\nhours score\n0 5.939322 68.764553\n1 7.436704 77.888040\n2 6.424870 74.196060\n3 5.903949 67.726441\n4 4.812893 72.849046<\/strong><\/pre>\n Ad\u0131m 3: Kantil Regresyon Ger\u00e7ekle\u015ftirin<\/strong><\/span><\/h3>\n
#fit the model<\/span>\nmodel = smf. quantreg<\/span> ('score~hours', df). fit<\/span> (q= 0.9<\/span> )\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n QuantReg Regression Results \n==================================================== ============================\nDept. Variable: Pseudo R-squared score: 0.6057\nModel: QuantReg Bandwidth: 3.822\nMethod: Least Squares Sparsity: 10.85\nDate: Tue, 29 Dec 2020 No. Observations: 100\nTime: 15:41:44 Df Residuals: 98\n Model: 1\n==================================================== ============================\n coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nIntercept 59.6104 0.748 79.702 0.000 58.126 61.095\nhours 2.8495 0.128 22.303 0.000 2.596 3.103\n==================================================== ============================<\/strong><\/span><\/pre>\n 4. Ad\u0131m: Sonu\u00e7lar\u0131 g\u00f6rselle\u015ftirin<\/strong><\/span><\/h3>\n
#define figure and axis\n<\/span>fig, ax = plt.subplots(figsize=(8, 6))\n\n#get y values\n<\/span>get_y = lambda<\/span> a, b: a + b * hours\ny = get_y( model.params<\/span> [' Intercept<\/span> '], model.params<\/span> [' hours<\/span> '])\n\n#plot data points with quantile regression equation overlaid\n<\/span>ax. plot<\/span> (hours, y, color=' black<\/span> ')\nax. scatter<\/span> (hours, score, alpha=.3)\nax. set_xlabel<\/span> (' Hours Studied<\/span> ', fontsize=14)\nax. set_ylabel<\/span> (' Exam Score<\/span> ', fontsize=14)\n<\/strong><\/span><\/pre>\n![\"Python'da](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/quantregpython1.png\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Python’da ikinci dereceden regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"