Yani yorday\u0131c\u0131 de\u011fi\u015fken artt\u0131k\u00e7a tepki de\u011fi\u015fkeni de artma e\u011filiminde olur ancak belli bir noktadan sonra yorday\u0131c\u0131 de\u011fi\u015fken artmaya devam ettik\u00e7e yan\u0131t de\u011fi\u015fkeni azalmaya ba\u015flar.<\/span><\/p>\n Bu e\u011fitimde Python’da ikinci dereceden regresyonun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n 16 farkl\u0131 ki\u015fi i\u00e7in haftada \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131na ve rapor edilen mutluluk d\u00fczeyine (0’dan 100’e kadar bir \u00f6l\u00e7ekte) ili\u015fkin verilerimiz oldu\u011funu varsayal\u0131m:<\/span><\/p>\n Bu verinin basit bir da\u011f\u0131l\u0131m grafi\u011fini yaparsak iki de\u011fi\u015fken aras\u0131ndaki ili\u015fkinin \u201cU\u201d \u015feklinde oldu\u011funu g\u00f6rebiliriz:<\/span> <\/p>\n \u00c7al\u0131\u015fma saatleri artt\u0131k\u00e7a mutluluk da art\u0131yor ancak \u00e7al\u0131\u015fma saatleri haftada yakla\u015f\u0131k 35 saati a\u015ft\u0131\u011f\u0131nda mutluluk azalmaya ba\u015fl\u0131yor.<\/span><\/p>\n Bu “U” \u015fekli nedeniyle bu, ikinci dereceden regresyonun iki de\u011fi\u015fken aras\u0131ndaki ili\u015fkiyi \u00f6l\u00e7mek i\u00e7in muhtemelen iyi bir aday oldu\u011fu anlam\u0131na gelir.<\/span><\/p>\n Ger\u00e7ekten ikinci dereceden bir regresyon ger\u00e7ekle\u015ftirmek i\u00e7in numpy.polyfit()<\/a> i\u015flevini<\/span> kullanarak derecesi 2 olan bir polinom regresyon modeli yerle\u015ftirebiliriz<\/span> : <\/p>\n Uygun polinom regresyon denklemini model katsay\u0131lar\u0131n\u0131 yazd\u0131rarak elde edebiliriz:<\/span><\/p>\n Uygun ikinci dereceden regresyon denklemi:<\/span><\/p>\n Mutluluk = -0,107(saat) 2<\/sup> + 7,173(saat) \u2013 30,25<\/strong><\/span><\/p>\n Bu denklemi, bireyin \u00e7al\u0131\u015fma saatlerine g\u00f6re beklenen mutluluk d\u00fczeyini hesaplamak i\u00e7in kullanabiliriz. \u00d6rne\u011fin haftada 30 saat \u00e7al\u0131\u015fan bir ki\u015finin beklenen mutluluk d\u00fczeyi:<\/span><\/p>\n Mutluluk = -0,107(30) 2<\/sup> + 7,173(30) \u2013 30,25 = 88,64<\/strong> .<\/span><\/p>\n Ayr\u0131ca, yorday\u0131c\u0131 de\u011fi\u015fkenler taraf\u0131ndan a\u00e7\u0131klanabilen yan\u0131t de\u011fi\u015fkenindeki varyans\u0131n oran\u0131 olan modelin R-karesini elde etmek i\u00e7in k\u0131sa bir fonksiyon da yazabiliriz.<\/span><\/p>\n Bu \u00f6rnekte modelin R karesi 0,9092’dir<\/strong> .<\/span><\/p>\n Bu, bildirilen mutluluk d\u00fczeylerindeki de\u011fi\u015fimin %90,92’sinin yorday\u0131c\u0131 de\u011fi\u015fkenler taraf\u0131ndan a\u00e7\u0131klanabilece\u011fi anlam\u0131na gelir.<\/span><\/p>\n Python’da polinom regresyonu nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> \u0130kinci dereceden regresyon, ger\u00e7ek ili\u015fkiler ikinci dereceden oldu\u011funda, bir grafikte “U” veya ters \u00e7evrilmi\u015f “U” gibi g\u00f6r\u00fcnebilen, bir tahmin de\u011fi\u015fkeni ile bir yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fkiyi \u00f6l\u00e7mek i\u00e7in kullanabilece\u011fimiz bir regresyon t\u00fcr\u00fcd\u00fcr. Yani yorday\u0131c\u0131 de\u011fi\u015fken artt\u0131k\u00e7a tepki de\u011fi\u015fkeni de artma e\u011filiminde olur ancak belli bir noktadan sonra yorday\u0131c\u0131 de\u011fi\u015fken artmaya devam ettik\u00e7e yan\u0131t de\u011fi\u015fkeni azalmaya […]<\/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-984","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n \u00d6rnek: Python’da \u0130kinci Dereceden Regresyon<\/span><\/strong><\/h2>\n
import<\/span> numpy as<\/span> np\nimport<\/span> scipy.stats as<\/span> stats\n\n#define variables<\/span>\nhours = [6, 9, 12, 12, 15, 21, 24, 24, 27, 30, 36, 39, 45, 48, 57, 60]\nhapp = [12, 18, 30, 42, 48, 78, 90, 96, 96, 90, 84, 78, 66, 54, 36, 24]<\/strong><\/pre>\n import matplotlib.pyplot<\/span> as plt<\/span>\n\n#create scatterplot\n<\/span>plt.scatter(hours, happ)<\/span><\/span><\/strong> <\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/quadregpython1.png\")
<\/p>\n import<\/span> numpy as<\/span> np\n\n#polynomial fit with degree = 2\n<\/span>model = np.poly1d(np.polyfit(hours, happ, 2))\n\n#add fitted polynomial line to scatterplot\n<\/span>polyline = np.linspace(1, 60, 50)\nplt.scatter(hours, happ)\nplt.plot(polyline, model(polyline))\nplt.show()<\/strong> <\/pre>\n![\"Python'da](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/quadregpython2.png\")
<\/p>\n print<\/span> (model)\n\n-0.107x 2<\/sup> + 7.173x - 30.25\n<\/strong><\/pre>\n #define function to calculate r-squared<\/span>\ndef<\/span> polyfit(x, y, degree):\n results = {}\n coeffs = np.polyfit(x, y, degree)\n p = np.poly1d(coeffs)\n #calculate r-squared<\/span>\n yhat = p(x)\n ybar = np.sum(y)\/len(y)\n ssreg = np.sum((yhat-ybar)**2)\n sstot = np.sum((y - ybar)**2)\n results['r_squared'] = ssreg \/ sstot\n\n return<\/span> results\n\n#find r-squared of polynomial model with degree = 3\n<\/span>polyfit(hours, happ, 2)\n\n{'r_squared': 0.9092114182131691}\n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
R’de ikinci dereceden regresyon nas\u0131l ger\u00e7ekle\u015ftirilir?<\/a>
Excel’de ikinci dereceden regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"