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--- c:ma:2016:schedule:week09_answer [2016/11/09 09:19] – hkimscil
+++ c:ma:2016:schedule:week09_answer [2016/11/09 09:52] (current) – [E.g. 6] hkimscil
@@ Line 298: / Line 298: @@
     - 테스트를 한 후
     - 결과를 보고하시오.
+<code>>anorexia
+. . . .
+> md = subset(anorexia, Treat=="FT")
+> md
+   Treat Prewt Postwt
+    FT  83.8   95.2
+    FT  83.3   94.3
+    FT  86.0   91.5
+    FT  82.5   91.9
+    FT  86.7  100.3
+    FT  79.6   76.7
+    FT  76.9   76.8
+    FT  94.2  101.6
+    FT  73.4   94.9
+    FT  80.5   75.2
+    FT  81.6   77.8
+    FT  82.1   95.5
+    FT  77.6   90.7
+    FT  83.5   92.5
+    FT  89.9   93.8
+    FT  86.0   91.7
+    FT  87.3   98.0
+> t.test(md$Prewt, md$Postwt, data=md, paired=TRUE)
+	Paired t-test
+data:  md$Prewt and md$Postwt
+t = -4.1849, df = 16, p-value = 0.0007003
+alternative hypothesis: true difference in means is not equal to 0
+percent confidence interval:
+ -10.94471  -3.58470
+sample estimates:
+mean of the differences
+              -7.264706
+</code>
 ====== E.g. 4 ======
 <WRAP box>
@@ Line 304: / Line 345: @@
 </WRAP>
 두 그룹의 평균의 차이를 비교하시오.
+<code>> a
+ [1] 175 168 168 190 156 181 182 175 174 179
+> b
+ [1] 185 169 173 173 188 186 175 174 179 180
+> ab <- data.frame(a,b)
+> ab
+     a   b
+  175 185
+  168 169
+  168 173
+  190 173
+  156 188
+  181 186
+  182 175
+  175 174
+  174 179
+179 180
+>
+> summary(ab)
+       a               b
+ Min.   :156.0   Min.   :169.0
+st Qu.:169.5   1st Qu.:173.2
+ Median :175.0   Median :177.0
+ Mean   :174.8   Mean   :178.2
+rd Qu.:180.5   3rd Qu.:183.8
+ Max.   :190.0   Max.   :188.0
+> abs <- stack(ab)
+> tapply(abs$values, abs$ind, summary)
+$a
+   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+.0   169.5   175.0   174.8   180.5   190.0
+$b
+   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+.0   173.2   177.0   178.2   183.8   188.0
+> tapply(abs$values, abs$ind, sd)
+       a        b
+.342852 6.442912
+>
+> t.test(ab$a,ab$b)
+	Welch Two Sample t-test
+data:  ab$a and ab$b
+t = -0.94737, df = 15.981, p-value = 0.3576
+alternative hypothesis: true difference in means is not equal to 0
+percent confidence interval:
+ -11.008795   4.208795
+sample estimates:
+mean of x mean of y
+.8     178.2
+</code>
 ====== E.g. 5 ======
@@ Line 310: / Line 409: @@
 아이스크림의 박테리아가 0.3 MPN/g 보다 커서 유통되기에 위험하다고 할 수 있을까?
+<code>> ir <- c(0.593, 0.142, 0.329, 0.691, 0.231, 0.793, 0.519, 0.392, 0.418)
+> ir
+[1] 0.593 0.142 0.329 0.691 0.231 0.793 0.519 0.392 0.418
+> t.test(ir, mu=.3)
+	One Sample t-test
+data:  ir
+t = 2.2051, df = 8, p-value = 0.05853
+alternative hypothesis: true mean is not equal to 0.3
+percent confidence interval:
+.2928381 0.6200508
+sample estimates:
+mean of x
+.4564444
+>
+> t.test(ir, alternative="greater", mu=.3)
+	One Sample t-test
+data:  ir
+t = 2.2051, df = 8, p-value = 0.02927
+alternative hypothesis: true mean is greater than 0.3
+percent confidence interval:
+.3245133       Inf
+sample estimates:
+mean of x
+.4564444
+>
+</code>
 ====== E.g. 6 ======
@@ Line 317: / Line 450: @@
 흡연이 기억에 영향을 준다고 할 수 있을까?
+<code>
+> smoke <- c(18,22,21,17,20,17,23,20,22,21)
+> nosmoke <- c(16,20,14,21,20,18,13,15,17,21)
+> sn <- data.frame(smoke, nosmoke)
+> ss <- stack(sn)
+> plot(ss$values~ss$ind)
+</code>
+<code>> t.test(values$ss~ind$ss)
+	Welch Two Sample t-test
+data:  ss$values by ss$ind
+t = -2.2573, df = 16.376, p-value = 0.03798
+alternative hypothesis: true difference in means is not equal to 0
+percent confidence interval:
+ -5.0371795 -0.1628205
+sample estimates:
+mean in group nosmoke   mean in group smoke
+.5                  20.1
+>
+>
+</code>
 ====== E.g. 7 ======
   - MASS package를 불러온 후, survey 데이터를 활용하여 담배와 운동량 간의 관계에 대한 가설테스트를 하시오.