<\/p>\n
R’de lojistik regresyon ger\u00e7ekle\u015ftirdi\u011finizde, regresyon modelinizin \u00e7\u0131kt\u0131s\u0131 a\u015fa\u011f\u0131daki formatta g\u00f6r\u00fcnt\u00fclenecektir:<\/span><\/p>\n Pr(>|z|)<\/strong> s\u00fctunu, z de\u011feri<\/strong> s\u00fctunundaki de\u011ferle ili\u015fkili p de\u011ferini temsil eder.<\/span><\/p>\n P de\u011ferinin belirli bir anlaml\u0131l\u0131k d\u00fczeyinin alt\u0131nda olmas\u0131 (\u00f6rne\u011fin \u03b1 = 0,05), yorday\u0131c\u0131 de\u011fi\u015fkenin modeldeki yan\u0131t de\u011fi\u015fkeni<\/a> ile istatistiksel olarak anlaml\u0131 bir ili\u015fkiye sahip oldu\u011funu g\u00f6sterir.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, pratikte lojistik regresyon modeli i\u00e7in Pr(>|z|) s\u00fctununun de\u011ferlerinin nas\u0131l yorumlanaca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, yerle\u015fik mtcars<\/strong> veri k\u00fcmesini kullanarak R’ye bir lojistik regresyon modelinin<\/a> nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Pr(>|z|) s\u00fctunundaki de\u011ferleri \u015fu \u015fekilde yorumlayabilirsiniz:<\/span><\/p>\n Katsay\u0131 tablosunun alt\u0131ndaki anlaml\u0131l\u0131k kodlar\u0131<\/a> bize, 0,0315 p de\u011ferinin yan\u0131ndaki tek y\u0131ld\u0131z i\u015faretinin (*), p de\u011ferinin \u03b1 = 0,05’te istatistiksel olarak anlaml\u0131 oldu\u011fu anlam\u0131na geldi\u011fini s\u00f6yler.<\/span><\/p>\n Pr(>|z|) de\u011ferinin asl\u0131nda nas\u0131l hesapland\u0131\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Ad\u0131m 1: Z de\u011ferini hesaplay\u0131n<\/strong><\/span><\/p>\n \u0130lk olarak z de\u011ferini<\/strong> a\u015fa\u011f\u0131daki form\u00fcl\u00fc kullanarak hesapl\u0131yoruz:<\/span><\/p>\n \u00d6rne\u011fin, tahmin de\u011fi\u015fkeni “drat” i\u00e7in z de\u011ferinin nas\u0131l hesaplanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Ad\u0131m 2: P de\u011ferini hesaplay\u0131n<\/strong><\/span><\/p>\n Daha sonra iki kuyruklu p de\u011ferini hesapl\u0131yoruz. Bu, normal da\u011f\u0131l\u0131m\u0131n mutlak de\u011ferinin 2,151’den b\u00fcy\u00fck veya -2,151’den k\u00fc\u00e7\u00fck olma olas\u0131l\u0131\u011f\u0131n\u0131 temsil eder.<\/span><\/p>\n Bu de\u011feri hesaplamak i\u00e7in R’de a\u015fa\u011f\u0131daki form\u00fcl\u00fc kullanabiliriz:<\/span><\/p>\n \u00d6rne\u011fin, 2,151 z de\u011feri i\u00e7in iki kuyruklu p de\u011ferinin nas\u0131l hesaplanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Bu p de\u011ferinin yukar\u0131daki regresyon \u00e7\u0131kt\u0131s\u0131ndaki p de\u011feriyle e\u015fle\u015fti\u011fini unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’ye farkl\u0131 regresyon modellerinin nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de lojistik regresyon nas\u0131l ger\u00e7ekle\u015ftirilir R’de lojistik regresyon ger\u00e7ekle\u015ftirdi\u011finizde, regresyon modelinizin \u00e7\u0131kt\u0131s\u0131 a\u015fa\u011f\u0131daki formatta g\u00f6r\u00fcnt\u00fclenecektir: Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -17.638452 9.165482 -1.924 0.0543 . available -0.004153 0.006621 -0.627 0.5305 drat 4.879396 2.268115 2.151 0.0315 * Pr(>|z|) s\u00fctunu, z de\u011feri s\u00fctunundaki de\u011ferle ili\u015fkili p de\u011ferini temsil eder. P de\u011ferinin belirli bir anlaml\u0131l\u0131k d\u00fczeyinin alt\u0131nda olmas\u0131 (\u00f6rne\u011fin \u03b1 […]<\/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-2713","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Coefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) -17.638452 9.165482 -1.924 0.0543 .\navailable -0.004153 0.006621 -0.627 0.5305 \ndrat 4.879396 2.268115 2.151 0.0315 * \n<\/span><\/span><\/strong><\/pre>\n \u00d6rnek: Pr(>|z|) de\u011ferleri nas\u0131l yorumlan\u0131r?<\/strong><\/span><\/h3>\n
#fit logistic regression model\n<\/span>model <- glm(am ~ disp + drat, data=mtcars, family=binomial)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nglm(formula = am ~ disp + drat, family = binomial, data = mtcars)\n\nDeviance Residuals: \n Min 1Q Median 3Q Max \n-1.5773 -0.2273 -0.1155 0.5196 1.8957 \n\nCoefficients:\n Estimate Std. Error z value Pr(>|z|) \n(Intercept) -17.638452 9.165482 -1.924 0.0543 .\navailable -0.004153 0.006621 -0.627 0.5305 \ndrat 4.879396 2.268115 2.151 0.0315 *\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\n(Dispersion parameter for binomial family taken to be 1)\n\n Null deviance: 43,230 on 31 degrees of freedom\nResidual deviance: 21,268 on 29 degrees of freedom\nAIC: 27,268\n\nNumber of Fisher Scoring iterations: 6<\/strong><\/span><\/pre>\n\n
Pr(>|z|) nas\u0131l hesaplan\u0131r?<\/strong><\/span><\/h3>\n
\n
#calculate z-value<\/span>\n4.879396 \/ 2.268115<\/span>\n\n[1] 2.151\n<\/strong><\/pre>\n\n
#calculate p-value<\/span>\n2*(1-pnorm(2.151))\n\n[1] 0.0314762\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"