R’ye bir regresyon \u00e7izgisi s\u0131\u011fd\u0131rmak amac\u0131yla en k\u00fc\u00e7\u00fck kareler y\u00f6ntemini kullanmak i\u00e7in lm()<\/strong> i\u015flevini kullanabiliriz.<\/span><\/p>\n Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek bu i\u015flevin R’de nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n R’de, bir s\u0131n\u0131ftaki 15 \u00f6\u011frencinin \u00e7al\u0131\u015f\u0131lan saat say\u0131s\u0131n\u0131 ve kar\u015f\u0131l\u0131k gelen s\u0131nav puan\u0131n\u0131 g\u00f6steren a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Bu verilere bir regresyon \u00e7izgisi s\u0131\u011fd\u0131rmak amac\u0131yla en k\u00fc\u00e7\u00fck kareler y\u00f6ntemini kullanmak i\u00e7in lm()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Tahmini<\/strong> Sonu\u00e7 s\u00fctunundaki de\u011ferlerden a\u015fa\u011f\u0131daki uygun regresyon sat\u0131r\u0131n\u0131 yazabiliriz:<\/span><\/p>\n S\u0131nav puan\u0131 = 65.334 + 1.982 (saat)<\/span><\/p>\n Modeldeki her bir katsay\u0131n\u0131n nas\u0131l yorumlanaca\u011f\u0131 a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Bir \u00f6\u011frencinin \u00e7al\u0131\u015fma saatlerine g\u00f6re alaca\u011f\u0131 s\u0131nav notunu tahmin etmek i\u00e7in bu denklemi kullanabiliriz.<\/span><\/p>\n \u00d6rne\u011fin bir \u00f6\u011frenci 5 saat \u00e7al\u0131\u015f\u0131yorsa s\u0131nav puan\u0131n\u0131n 75.244 olaca\u011f\u0131n\u0131 tahmin ediyoruz:<\/span><\/p>\n S\u0131nav Puan\u0131 = 65,334 + 1,982(5) = 75,244<\/span><\/p>\n Son olarak, arsa \u00fczerine bindirilmi\u015f uygun regresyon \u00e7izgisiyle orijinal verilerin bir da\u011f\u0131l\u0131m grafi\u011fini olu\u015fturabiliriz:<\/span> <\/p>\n Mavi daireler verileri, siyah \u00e7izgi ise tak\u0131lan regresyon \u00e7izgisini temsil eder.<\/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 art\u0131k arsa nas\u0131l olu\u015fturulur<\/a> En k\u00fc\u00e7\u00fck kareler y\u00f6ntemi, belirli bir veri k\u00fcmesine en iyi uyan regresyon do\u011frusunu bulmak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir. R’ye bir regresyon \u00e7izgisi s\u0131\u011fd\u0131rmak amac\u0131yla en k\u00fc\u00e7\u00fck kareler y\u00f6ntemini kullanmak i\u00e7in lm() i\u015flevini kullanabiliriz. Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r: model <- lm(response ~ predictor, data=df) A\u015fa\u011f\u0131daki \u00f6rnek bu i\u015flevin R’de nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. \u00d6rnek: R’de […]<\/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-3907","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n model <- lm(response ~ predictor, data=df)\n<\/strong><\/pre>\n
\u00d6rnek: R’de en k\u00fc\u00e7\u00fck kareler y\u00f6ntemi<\/strong><\/span><\/h2>\n
#create data frame\n<\/span>df <- data. frame<\/span> (hours=c(1, 2, 4, 5, 5, 6, 6, 7, 8, 10, 11, 11, 12, 12, 14),\n score=c(64, 66, 76, 73, 74, 81, 83, 82, 80, 88, 84, 82, 91, 93, 89))\n\n#view first six rows of data frame\n<\/span>head(df)\n\n hours score\n1 1 64\n2 2 66\n3 4 76\n4 5 73\n5 5 74\n6 6 81\n<\/strong><\/pre>\n #use method of least squares to fit regression line\n<\/span>model <- lm(score ~ hours, data=df)\n\n#view regression model summary<\/span>\nsummary(model)\n\nCall:\nlm(formula = score ~ hours, data = df)\n\nResiduals:\n Min 1Q Median 3Q Max \n-5,140 -3,219 -1,193 2,816 5,772 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 65,334 2,106 31,023 1.41e-13 ***\nhours 1.982 0.248 7.995 2.25e-06 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.641 on 13 degrees of freedom\nMultiple R-squared: 0.831, Adjusted R-squared: 0.818 \nF-statistic: 63.91 on 1 and 13 DF, p-value: 2.253e-06\n<\/strong><\/pre>\n\n
#create scatter plot of data\n<\/span>plot(df$hours, df$score, pch=16, col=' steelblue<\/span> ')\n\n#add fitted regression line to scatter plot\n<\/span>abline(model)\n<\/strong><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/methode1.jpg\")
<\/p>\n Ek kaynaklar<\/span><\/strong><\/h2>\n
R’de \u00e7oklu do\u011frusall\u0131k nas\u0131l test edilir<\/a>
R’de e\u011fri uydurma nas\u0131l ger\u00e7ekle\u015ftirilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"