sampling_distribution_in_r

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sampling_distribution_in_r [2025/03/24 08:49] hkimscilsampling_distribution_in_r [2025/03/24 09:00] (current) hkimscil
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 ====== Sampling distribution in R e.g. 1 ====== ====== Sampling distribution in R e.g. 1 ======
 <code> <code>
 +# sampling distribution 
 n.ajstu <- 100000 n.ajstu <- 100000
 mean.ajstu <- 100 mean.ajstu <- 100
Line 77: Line 78:
   ses[i] = sqrt(var(ajstu)/sss[i])   ses[i] = sqrt(var(ajstu)/sss[i])
 } }
 +ses.means4 <- sqrt(var(means4))
 +ses.means25 <- sqrt(var(means25))
 +ses.means100 <- sqrt(var(means100))
 +ses.means400 <- sqrt(var(means400))
 +ses.means900 <- sqrt(var(means900))
 +ses.means1600 <- sqrt(var(means1600))
 +ses.means2500 <- sqrt(var(means2500))
 +ses.real <- c(ses.means4, ses.means25,
 +              ses.means100, ses.means400,
 +              ses.means900, ses.means1600,
 +              ses.means2500)
 +ses.real 
  
 ses ses
Line 84: Line 97:
 lower.s2 <- mean(ajstu)-se.2 lower.s2 <- mean(ajstu)-se.2
 upper.s2 <- mean(ajstu)+se.2 upper.s2 <- mean(ajstu)+se.2
-data.frame(cbind(sss, ses, lower.s2, upper.s2)) +data.frame(cbind(sss, ses, ses.real, lower.s2, upper.s2))
 </code> </code>
 +아웃풋
 <code> <code>
-> # sampling distribution  
 > n.ajstu <- 100000 > n.ajstu <- 100000
 > mean.ajstu <- 100 > mean.ajstu <- 100
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 6 1600 0.2500000 99.50000 100.5000 6 1600 0.2500000 99.50000 100.5000
 7 2500 0.2000000 99.60000 100.4000 7 2500 0.2000000 99.60000 100.4000
 +> sss <- c(4,25,100,400,900,1600,2500) # sss sample sizes
 +> ses <- rep (NA, length(sss)) # std errors
 +> for(i in 1:length(sss)){
 ++   ses[i] = sqrt(var(ajstu)/sss[i])
 ++ }
 +> ses.means4 <- sqrt(var(means4))
 +> ses.means25 <- sqrt(var(means25))
 +> ses.means100 <- sqrt(var(means100))
 +> ses.means400 <- sqrt(var(means400))
 +> ses.means900 <- sqrt(var(means900))
 +> ses.means1600 <- sqrt(var(means1600))
 +> ses.means2500 <- sqrt(var(means2500))
 +> ses.real <- c(ses.means4, ses.means25,
 ++               ses.means100, ses.means400,
 ++               ses.means900, ses.means1600,
 ++               ses.means2500)
 +> ses.real 
 +[1] 4.9719142 2.0155741 0.9999527 0.5034433 0.3324414 0.2466634
 +[7] 0.1965940
 +> ses
 +[1] 5.0000000 2.0000000 1.0000000 0.5000000 0.3333333 0.2500000
 +[7] 0.2000000
 +> se.1 <- ses
 +> se.2 <- 2 * ses 
 +> lower.s2 <- mean(ajstu)-se.2
 +> upper.s2 <- mean(ajstu)+se.2
 +> data.frame(cbind(sss, ses, ses.real, lower.s2, upper.s2))
 +   sss       ses  ses.real lower.s2 upper.s2
 +1    4 5.0000000 4.9719142 90.00000 110.0000
 +2   25 2.0000000 2.0155741 96.00000 104.0000
 +3  100 1.0000000 0.9999527 98.00000 102.0000
 +4  400 0.5000000 0.5034433 99.00000 101.0000
 +5  900 0.3333333 0.3324414 99.33333 100.6667
 +6 1600 0.2500000 0.2466634 99.50000 100.5000
 +7 2500 0.2000000 0.1965940 99.60000 100.4000
  
 </code> </code>
sampling_distribution_in_r.1742773773.txt.gz · Last modified: 2025/03/24 08:49 by hkimscil

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