diff --git a/1.Correllation.r b/Correllation/1.Correllation.r
similarity index 100%
rename from 1.Correllation.r
rename to Correllation/1.Correllation.r
diff --git a/2.Recursive.r b/Correllation/2.Recursive.r
similarity index 100%
rename from 2.Recursive.r
rename to Correllation/2.Recursive.r
diff --git a/Logistic Regression/1. LR.r b/Logistic Regression/1. LR.r
new file mode 100644
index 0000000..6bbc21f
--- /dev/null
+++ b/Logistic Regression/1. LR.r	
@@ -0,0 +1,24 @@
+dose=c(1,1,2,2,3,3)
+response=c(0,1,0,1,0,1)
+count=c(7,3,5,5,2,8)
+
+toxic = data.frame(dose, response, count)
+toxic
+
+out = glm(response~dose,
+          weights = count,
+          family = binomial,
+          data = toxic)
+summary(out)
+
+# Coefficients:
+#             Estimate Std. Error z value Pr(>|z|)
+# (Intercept)  -2.0496     1.0888  -1.882   0.0598 .
+# dose          1.1051     0.5186   2.131   0.0331 *
+
+# dose B = 1.1051 => Exp(1.1051) = 3.02
+# dose 1 증가시 반응성 3.02배 증가한다고 볼 수 있음
+# p-value = 0.03 < 0.05, 귀무가설 기각 => 약의 종류에 따라 반응이 다를 수 있음
+
+plot(response~dose, data=toxic, type="n", main="Predicted Probability of Response")
+curve(predict(out, data.frame(dose=x), type="resp"), add=TRUE)
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diff --git a/PCA/1. pca.r b/PCA/1. pca.r
new file mode 100644
index 0000000..dc73e54
--- /dev/null
+++ b/PCA/1. pca.r	
@@ -0,0 +1,23 @@
+x1=c(26,46,57,36,57,26,58,37,36,56,78,95,88,90,52,56)
+x2=c(35,74,73,73,62,22,67,34,22,42,65,88,90,85,46,66)
+x3=c(35,76,38,69,25,25,87,79,36,26,22,36,58,36,25,44)
+x4=c(45,89,54,55,33,45,67,89,47,36,40,56,68,45,37,56)
+
+score = cbind(x1, x2, x3, x4)
+colnames(score) = c("국어", "영어", "수학", "과학")
+rownames(score) = 1:16
+head(score)
+
+result = prcomp(score) #주성분 고유벡터 출력
+result
+
+summary(result)
+
+# Importance of components:
+#                            PC1     PC2     PC3     PC4
+# Standard deviation     30.1227 27.0528 9.07614 6.15239
+# Proportion of Variance  0.5157  0.4159 0.04682 0.02151
+# Cumulative Proportion   0.5157  0.9317 0.97849 1.00000
+
+# PC2까지 누적 기여율 93.17%로 2개의 주성분 결정
+# (일반적으로 80% 이상 되는 지점의 성분수를 주성분으로 결정함)
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diff --git a/analysis of variance/1. aov.r b/analysis of variance/1. aov.r
new file mode 100644
index 0000000..880e59d
--- /dev/null
+++ b/analysis of variance/1. aov.r	
@@ -0,0 +1,36 @@
+install.packages("car")
+library(car)
+
+data(anorexia, package = "MASS")
+anorexia
+
+# levene 테스트로 등분산성 확인
+leveneTest(Postwt~Treat, data = anorexia)
+
+# > leveneTest(Postwt~Treat, data = anorexia)
+# Levene's Test for Homogeneity of Variance (center = median)
+#       Df F value Pr(>F)
+# group  2  1.7671 0.1785
+#       69
+# Pr(>F) = 0.1785, 0.05보다 크므로 귀무가설 채택 (등분산성 만족)
+
+# 분산분석
+out1 = aov(Postwt~Treat, data = anorexia)
+out1
+summary(out1)
+
+# > summary(out1)
+#             Df Sum Sq Mean Sq F value   Pr(>F)    
+# Treat        2    919   459.5   8.651 0.000444 ***
+# Residuals   69   3665    53.1
+# p-value 가 0.05보다 작으므로 귀무가설 기각 (각 집단 평균에 차이가 있음)
+
+# ANOVA 분석
+out2 = anova(lm(Postwt~Treat, data = anorexia))
+out2
+summary(out2)
+
+# 일원 분산분석
+out3 = oneway.test(Postwt~Treat, data = anorexia)
+out3
+summary(out3)
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