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r:drawing_sampling_distribution_plot

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r:drawing_sampling_distribution_plot [2025/09/11 07:24] hkimscilr:drawing_sampling_distribution_plot [2025/09/11 07:32] (current) hkimscil
Line 13: Line 13:
 sd.p1 <- sd(p1) sd.p1 <- sd(p1)
  
-p2 <- rnorm2(n.p, m.p+10, sd.p)+p2 <- rnorm2(n.p, m.p+5, sd.p)
 m.p2 <- mean(p2) m.p2 <- mean(p2)
 sd.p2 <- sd(p2) sd.p2 <- sd(p2)
Line 40: Line 40:
 se3 <- c(m.p1-3*se.z, m.p1+3*se.z) se3 <- c(m.p1-3*se.z, m.p1+3*se.z)
 abline(v=c(m.p1,se1,se2,se3),  abline(v=c(m.p1,se1,se2,se3), 
-       col=c('black', 'red', 'red', +       col=c('black', 'orange', 'orange', 
              'green', 'green',               'green', 'green', 
              'blue', 'blue'),               'blue', 'blue'), 
Line 47: Line 47:
 treated.s <- sample(p2, n.s) treated.s <- sample(p2, n.s)
 m.treated.s <- mean(treated.s) m.treated.s <- mean(treated.s)
-abline(v=m.treated.s, col='orange', lwd=2)+abline(v=m.treated.s, col='red', lwd=2) 
 + 
 +se.z
  
 diff <- m.treated.s-mean(p1) diff <- m.treated.s-mean(p1)
Line 65: Line 67:
  
 <code> <code>
 +
 > rm(list=ls()) > rm(list=ls())
  
Line 78: Line 81:
 > sd.p1 <- sd(p1) > sd.p1 <- sd(p1)
  
-> p2 <- rnorm2(n.p, m.p+10, sd.p)+> p2 <- rnorm2(n.p, m.p+5, sd.p)
 > m.p2 <- mean(p2) > m.p2 <- mean(p2)
 > sd.p2 <- sd(p2) > sd.p2 <- sd(p2)
Line 105: Line 108:
 > se3 <- c(m.p1-3*se.z, m.p1+3*se.z) > se3 <- c(m.p1-3*se.z, m.p1+3*se.z)
 > abline(v=c(m.p1,se1,se2,se3),  > abline(v=c(m.p1,se1,se2,se3), 
-+        col=c('black', 'red', 'red', ++        col=c('black', 'orange', 'orange', 
 +              'green', 'green',  +              'green', 'green', 
 +              'blue', 'blue'),  +              'blue', 'blue'), 
Line 112: Line 115:
 > treated.s <- sample(p2, n.s) > treated.s <- sample(p2, n.s)
 > m.treated.s <- mean(treated.s) > m.treated.s <- mean(treated.s)
-> abline(v=m.treated.s, col='orange', lwd=2)+> abline(v=m.treated.s, col='red', lwd=2) 
 +>  
 +> se.z 
 +[1] 1
  
 > diff <- m.treated.s-mean(p1) > diff <- m.treated.s-mean(p1)
 > diff/se.z > diff/se.z
-[1] 11.71789+[1] 5.871217
  
 > # usual way - using sample's variance  > # usual way - using sample's variance 
Line 123: Line 129:
 > se.s <- sqrt(var(treated.s)/n.s) > se.s <- sqrt(var(treated.s)/n.s)
 > se.s > se.s
-[1] 0.9903093+[1] 0.9861042
 > diff/se.s > diff/se.s
-[1] 11.83256+[1] 5.953951
  
 > pt(diff/se.s, df=n.s-1, lower.tail = F) * 2 > pt(diff/se.s, df=n.s-1, lower.tail = F) * 2
-[1] 1.171008e-20+[1] 3.994557e-08
 > t.test(treated.s, mu=m.p1, var.equal = T) > t.test(treated.s, mu=m.p1, var.equal = T)
  
Line 134: Line 140:
  
 data:  treated.s data:  treated.s
-t = 11.833, df = 99, p-value < 2.2e-16+t = 5.954, df = 99, p-value = 3.995e-08
 alternative hypothesis: true mean is not equal to 100 alternative hypothesis: true mean is not equal to 100
 95 percent confidence interval: 95 percent confidence interval:
- 109.7529 113.6829+ 103.9146 107.8279
 sample estimates: sample estimates:
 mean of x  mean of x 
- 111.7179 + 105.8712 
  
  
-> 
 </code> </code>
-{{:r:pasted:20250911-072403.png}}+{{:r:pasted:20250911-073157.png}} 
r/drawing_sampling_distribution_plot.txt · Last modified: 2025/09/11 07:32 by hkimscil

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