<\/p>\n
R’deki lm() i\u015flevinden<\/a> regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131karmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: yaln\u0131zca regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Standart hata, T istatisti\u011fi ve P de\u011ferleri ile regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu y\u00f6ntemlerin pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n R’ye a\u015fa\u011f\u0131daki \u00e7oklu do\u011frusal regresyon modelini uydu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Regresyon modelinin tam \u00f6zetini g\u00f6r\u00fcnt\u00fclemek i\u00e7in Summary()<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/span><\/p>\n Yaln\u0131zca regresyon katsay\u0131lar\u0131n\u0131 g\u00f6r\u00fcnt\u00fclemek i\u00e7in model$ katsay\u0131lar\u0131n\u0131<\/strong> a\u015fa\u011f\u0131daki gibi kullanabiliriz:<\/span><\/p>\n Bu katsay\u0131lar\u0131 a\u015fa\u011f\u0131daki uygun regresyon denklemini yazmak i\u00e7in kullanabiliriz:<\/span><\/p>\n Skor = 66,43551 + 1,21520 (say\u0131) \u2013 2,59678 (asist) + 2,82022 (ribaund)<\/span><\/p>\n Regresyon katsay\u0131lar\u0131n\u0131 standart hatalar\u0131, t istatistikleri ve p de\u011ferleri ile birlikte g\u00f6r\u00fcnt\u00fclemek i\u00e7in \u00f6zet(model)$ katsay\u0131lar\u0131n\u0131<\/strong> a\u015fa\u011f\u0131daki gibi kullanabiliriz:<\/span><\/p>\n Bu \u00e7\u0131kt\u0131da belirli de\u011ferlere de ula\u015fabiliyoruz.<\/span><\/span><\/p>\n \u00d6rne\u011fin puan<\/strong> de\u011fi\u015fkeninin p de\u011ferine<\/strong> eri\u015fmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Veya regresyon katsay\u0131lar\u0131n\u0131n her birinin p de\u011ferine eri\u015fmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Modeldeki her regresyon katsay\u0131s\u0131 i\u00e7in P de\u011ferleri g\u00f6sterilmektedir.<\/span><\/span><\/p>\n Regresyon \u00e7\u0131kt\u0131s\u0131ndaki herhangi bir de\u011fere eri\u015fmek i\u00e7in benzer s\u00f6zdizimini kullanabilirsiniz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’de di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de basit do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a> R’deki lm() i\u015flevinden regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131karmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: yaln\u0131zca regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n model$coefficients Y\u00f6ntem 2: Standart hata, T istatisti\u011fi ve P de\u011ferleri ile regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n summary(model)$coefficients A\u015fa\u011f\u0131daki \u00f6rnekte bu y\u00f6ntemlerin pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir. \u00d6rnek: R’deki lm() \u00f6\u011fesinden regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n R’ye a\u015fa\u011f\u0131daki \u00e7oklu do\u011frusal regresyon modelini uydu\u011fumuzu varsayal\u0131m: #create […]<\/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-2947","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n model$coefficients\n<\/strong><\/pre>\n
summary(model)$coefficients<\/strong><\/pre>\n
\u00d6rnek: R’deki lm() \u00f6\u011fesinden regresyon katsay\u0131lar\u0131n\u0131 \u00e7\u0131kar\u0131n<\/strong><\/span><\/h3>\n
#create data frame\n<\/span>df <- data. frame<\/span> (rating=c(67, 75, 79, 85, 90, 96, 97),\n points=c(8, 12, 16, 15, 22, 28, 24),\n assists=c(4, 6, 6, 5, 3, 8, 7),\n rebounds=c(1, 4, 3, 3, 2, 6, 7))\n\n#fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n<\/strong><\/pre>\n #view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ points + assists + rebounds, data = df)\n\nResiduals:\n 1 2 3 4 5 6 7 \n-1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 66.4355 6.6932 9.926 0.00218 **\npoints 1.2152 0.2788 4.359 0.02232 * \nassists -2.5968 1.6263 -1.597 0.20860 \nrebounds 2.8202 1.6118 1.750 0.17847 \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.193 on 3 degrees of freedom\nMultiple R-squared: 0.9589, Adjusted R-squared: 0.9179 \nF-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396\n<\/strong><\/pre>\n #view only regression coefficients of model\nmodel$coefficients\n\n(Intercept) points assists rebounds \n 66.435519 1.215203 -2.596789 2.820224<\/span><\/span>\n<\/strong><\/pre>\n #view regression coefficients with standard errors, t-statistics, and p-values\n<\/span>summary(model)$coefficients\n\n Estimate Std. Error t value Pr(>|t|)\n(Intercept) 66.435519 6.6931808 9.925852 0.002175313\npoints 1.215203 0.2787838 4.358942 0.022315418\nassists -2.596789 1.6262899 -1.596757 0.208600183\nrebounds 2.820224 1.6117911 1.749745 0.178471275<\/strong><\/pre>\n #view p-value for points variable\n<\/span>summary(model)$coefficients[\" points<\/span> \", \" Pr(>|t|)<\/span> \"]\n\n[1] 0.02231542\n<\/strong><\/pre>\n #view p-value for all variables\n<\/span>summary(model)$coefficients[, \" Pr(>|t|)<\/span> \"]\n\n(Intercept) points assists rebounds \n0.002175313 0.022315418 0.208600183 0.178471275 \n<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de \u00e7oklu do\u011frusal regresyon nas\u0131l ger\u00e7ekle\u015ftirilir<\/a>
R’de art\u0131k arsa nas\u0131l olu\u015fturulur<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"