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--- beta_coefficients [2020/12/09 09:42] – [e.g.] hkimscil
+++ beta_coefficients [2026/06/14 23:44] (current) – hkimscil
@@ Line 9: / Line 9: @@
 <code>
 # import test score data "tests_cor.csv"
-tests <- read.csv("http://commres.net/wiki/_media/r/tests_cor.csv")
+tests <- read.csv("http://commres.net/_media/r/tests_cor.csv")
 colnames(tests) <- c("ser", "sat", "clep", "gpa")
 tests <- subset(tests, select=c("sat", "clep", "gpa"))
@@ Line 88: / Line 88: @@
 <code>
 # get marketing data
-marketing <- read.csv("http://commres.net/wiki/_media/marketing_from_datarium.csv")
+marketing <- read.csv("http://commres.net/_media/marketing_from_datarium.csv")
 head(marketing)
 # note that I need - X to get rid of X column in the marketing data
@@ Line 96: / Line 96: @@
 <code>
-> marketing <- read.csv("http://commres.net/wiki/_media/marketing_from_datarium.csv")
+> marketing <- read.csv("http://commres.net/_media/marketing_from_datarium.csv")
 > head(marketing)
   X youtube facebook newspaper sales
@@ Line 188: / Line 188: @@
 >
 > </code>
+check out that
+''lm.beta(mod) == coefficients(mod.scaled)''
+and
+베타를 구하고 나면 서로의 계수값을 절대비교할 수 있다.
+''lm.beta(mod)''의 아웃풋을 보고
+''youtube'', ''facebook'', and ''newspaper'' 순으로 설명력을 갖는다고 말할 수 있다 (혹은 newspaper를 분석에서 제외하고 다시 분석하여 둘만의 설명력을 보는 것도 방법이다.