<\/p>\n
R’de do\u011frusal bir regresyon ger\u00e7ekle\u015ftirdi\u011finizde, regresyon modelinizin \u00e7\u0131kt\u0131s\u0131 a\u015fa\u011f\u0131daki formatta g\u00f6r\u00fcnt\u00fclenecektir:<\/span><\/p>\n Pr(>|t|)<\/strong> s\u00fctunu, t de\u011feri<\/strong> s\u00fctunundaki de\u011ferle ili\u015fkili p de\u011ferini temsil eder.<\/span><\/p>\n P de\u011feri belirli bir anlaml\u0131l\u0131k d\u00fczeyinin alt\u0131ndaysa (\u00f6rne\u011fin \u03b1 = 0,05), yorday\u0131c\u0131 de\u011fi\u015fkenin modeldeki yan\u0131t de\u011fi\u015fkeni ile istatistiksel olarak anlaml\u0131 bir ili\u015fkiye sahip oldu\u011fu kabul edilir.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, belirli bir regresyon modeli i\u00e7in Pr(>|t|) s\u00fctunundaki de\u011ferlerin nas\u0131l yorumlanaca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n Tahmin edici de\u011fi\u015fkenler x1<\/strong> ve x2’yi<\/strong> ve tek bir yan\u0131t de\u011fi\u015fkeni y’yi<\/strong> kullanarak \u00e7oklu do\u011frusal regresyon modeline<\/a> uymak istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, bir veri \u00e7er\u00e7evesinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 ve verilere bir regresyon modelinin nas\u0131l s\u0131\u011fd\u0131r\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Pr(>|t|) s\u00fctunundaki de\u011ferleri \u015fu \u015fekilde yorumlayabilirsiniz:<\/span><\/p>\n Katsay\u0131 tablosunun alt\u0131ndaki anlaml\u0131l\u0131k kodlar\u0131<\/a> bize, 0,0325 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(>|t|) de\u011ferinin ger\u00e7ekte nas\u0131l hesapland\u0131\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Ad\u0131m 1: t de\u011ferini hesaplay\u0131n<\/strong><\/span><\/p>\n \u00d6ncelikle a\u015fa\u011f\u0131daki form\u00fcl\u00fc kullanarak t de\u011ferini<\/strong> hesapl\u0131yoruz:<\/span><\/p>\n \u00d6rne\u011fin, tahmin de\u011fi\u015fkeni x1 i\u00e7in t 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 p-de\u011ferini hesapl\u0131yoruz. Bu, t da\u011f\u0131l\u0131m\u0131n\u0131n mutlak de\u011ferinin 2,935’ten b\u00fcy\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, 5 art\u0131k serbestlik derecesi ile 2,935’lik bir t de\u011feri i\u00e7in 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 Not:<\/strong> Art\u0131k serbestlik derecelerinin de\u011feri regresyon \u00e7\u0131kt\u0131s\u0131n\u0131n alt\u0131ndad\u0131r. \u00d6rne\u011fimizde 5 oldu\u011fu ortaya \u00e7\u0131kt\u0131:<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> R’de do\u011frusal bir regresyon ger\u00e7ekle\u015ftirdi\u011finizde, regresyon modelinizin \u00e7\u0131kt\u0131s\u0131 a\u015fa\u011f\u0131daki formatta g\u00f6r\u00fcnt\u00fclenecektir: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 10.0035 5.9091 1.693 0.1513 x1 1.4758 0.5029 2.935 0.0325 * x2 -0.7834 0.8014 -0.978 0.3732 Pr(>|t|) s\u00fctunu, t de\u011feri s\u00fctunundaki de\u011ferle ili\u015fkili p de\u011ferini temsil eder. P de\u011feri belirli bir anlaml\u0131l\u0131k d\u00fczeyinin alt\u0131ndaysa (\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-1764","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Coefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 10.0035 5.9091 1.693 0.1513 \nx1 1.4758 0.5029 2.935 0.0325 *\nx2 -0.7834 0.8014 -0.978 0.3732<\/span> \n<\/span><\/strong><\/pre>\n \u00d6rnek: Pr(>|t|) de\u011ferleri nas\u0131l yorumlan\u0131r?<\/strong><\/span><\/h3>\n
#create data frame\n<\/span>df <- data. frame<\/span> (x1=c(1, 3, 3, 4, 4, 5, 6, 6),\n x2=c(7, 7, 5, 6, 5, 4, 5, 6),\n y=c(8, 8, 9, 9, 13, 14, 17, 14))\n\n#fit multiple linear regression model\n<\/span>model <- lm(y ~ x1 + x2, data=df)\n\n#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = y ~ x1 + x2, data = df)\n\nResiduals:\n 1 2 3 4 5 6 7 8 \n 2.0046 -0.9470 -1.5138 -2.2062 1.0104 -0.2488 2.0588 -0.1578 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 10.0035 5.9091 1.693 0.1513 \nx1 1.4758 0.5029 2.935 0.0325 *\nx2 -0.7834 0.8014 -0.978 0.3732 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.867 on 5 degrees of freedom\nMultiple R-squared: 0.7876, Adjusted R-squared: 0.7026 \nF-statistic: 9.268 on 2 and 5 DF, p-value: 0.0208<\/strong><\/span><\/pre>\n\n
Pr(>|t|) ger\u00e7ekte nas\u0131l hesaplan\u0131r?<\/strong><\/span><\/h3>\n
\n
#calculate t-value<\/span>\n1.4758 \/ .5029\n\n[1] 2.934579\n<\/strong><\/pre>\n\n
#calculate p-value<\/span>\n2 * pt( abs<\/span> (2.935), 5, lower. tail<\/span> = FALSE<\/span> )\n\n[1] 0.0324441\n<\/strong><\/span><\/pre>\n Residual standard error: 1.867 on 5<\/span> degrees of freedom\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de \u00e7oklu do\u011frusal regresyon sonu\u00e7lar\u0131 nas\u0131l \u00e7izilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"