Differences

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--- b:head_first_statistics:using_the_normal_distribution [2025/10/26 23:26] – [Swivel chair again] hkimscil
+++ b:head_first_statistics:using_the_normal_distribution [2025/10/29 02:12] (current) – [All aboard the Love Train] hkimscil
@@ Line 146: / Line 146: @@
 [1] 0.9406201
 > pnorm(-1.56, lower.tail = FALSE)
+[1] 0.9406201
+> pnorm(-1.56, 0, 1, lower.tail = F)
 [1] 0.9406201
 > pnorm(64, 71, sqrt(20.25), lower.tail = FALSE)
@@ Line 828: / Line 830: @@
 <WRAP box>
-이를 R을 이용하여 구하면,
+위를 R에서 해보면
 <code>
-pbinom(5, 12, 1/2)
+> dbinom(0, 12, 1/2) + dbinom(1, 12, 1/2) + dbinom(2, 12, 1/2)
+>  + dbinom(3, 12, 1/2) + dbinom(4, 12, 1/2) + dbinom(5, 12, 1/2)
+[1] 0.387207
 </code>
+그러나, R에서는 더 간단한 방법으로
 <code>
 > pbinom(5, 12, 1/2)
@@ Line 838: / Line 842: @@
 </code>
-위는 아래와 같음을 이해해야 한다
+그리고, 위의 dbinom으로 하나씩 계산한다고 하더라도 아래처럼 하게 된다
 <code>
 > sum(dbinom(c(0:5),12,1/2))
@@ Line 925: / Line 929: @@
 <wrap #continuity_correction_egs />
 <WRAP box>
-X < 3  ----  <wrap spoiler> X < 2.5 </wrap>
+X < 3   <wrap spoiler> X < 2.5 </wrap>
-X > 3  ----  <wrap spoiler> X > 3.5 </wrap>
+X > 3   <wrap spoiler> X > 3.5 </wrap>
-X <_ 3  ----  <wrap spoiler> X < 3.5 </wrap>
+X <_ 3   <wrap spoiler> X < 3.5 </wrap>
-X >_ 3  ----  <wrap spoiler> X > 2.5 </wrap>
+X >_ 3   <wrap spoiler> X > 2.5 </wrap>
-<_ X < 10   ----  <wrap spoiler> 2.5 < X < 9.5 </wrap>
+<_ X < 10    <wrap spoiler> 2.5 < X < 9.5 </wrap>
-X = 0  ----  <wrap spoiler> -0.5 < X < 0.5 </wrap>
+X = 0   <wrap spoiler> -0.5 < X < 0.5 </wrap>
-<_ X <_ 10  ----  <wrap spoiler> 2.5 < X < 10.5 </wrap>
+<_ X <_ 10   <wrap spoiler> 2.5 < X < 10.5 </wrap>
-< X <_ 10  ----  <wrap spoiler> 3.5 < X < 10.5 </wrap>
+< X <_ 10   <wrap spoiler> 3.5 < X < 10.5 </wrap>
-X > 0  ----  <wrap spoiler> X > 0.5 </wrap>
+X > 0   <wrap spoiler> X > 0.5 </wrap>
-< X < 10  ----  <wrap spoiler> 3.5 < X < 9.5 </wrap>
+< X < 10   <wrap spoiler> 3.5 < X < 9.5 </wrap>
 </WRAP>
@@ Line 971: / Line 975: @@
 {{:b:head_first_statistics:pasted:20191118-113020.png}}
-$\lambda > 15$ 일 때, Poisson distribution, $X \sim Po(\lambda)$는 $X \sim N(\lambda, \lambda)$ 의 성격을 취한다.
+<fc #ff0000>$\lambda > 15$ 일 때,</fc> Poisson distribution, $X \sim Po(\lambda)$는 $X \sim N(\lambda, \lambda)$ 의 성격을 취한다.
 예)
@@ Line 1020: / Line 1024: @@
 $0.9654916 \sim 0.9656205$
+R에서 ppois을 이용하면
+<code>
+> ppois(51, 40)
+[1] 0.9612598
+>
+</code>
 ===== Check up =====