econstatproj1

# econstatproj1 - histogram is more like a normal graph,...

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F <- function(x){ product = x*(x+1)/2 return(product) } F(10) F(100) F(1000) G <- function(x){ product = x*(x+1)*(2*x+1)/6 return(product) } G(10) G(100) G(1000) sum = 0 for(i in 1:10){ sum = sum + i } sum sum = 0 for(i in 1:100){ sum = sum + i } sum sum = 0 for(i in 1:1000){ sum = sum + i } sum sum = 0 for(i in 1:10){ sum = sum + i*i } sum sum = 0 for(i in 1:100){ sum = sum + i*i } sum sum = 0

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for(i in 1:1000){ sum = sum + i*i } sum # f(n) and h(n) are identical except h(n) is in sigma notation, g(n) and l(n) are identical except l(n) is in sigma notation nreps = 1000 nObs= 100 average<-matrix(0, nrow=nreps, ncol=1) for(i in 1:nreps) { sample <- rnorm(nObs, 2, 2) average[i] = mean(sample) } mean(average) var(average) hist(average) # the mean is still close to 2, the variance has shrunk considerably, and the
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Unformatted text preview: histogram is more like a normal graph, meaning the theoretical prediction is closer with 1000 samples nreps = 1000 nObs= 1000 average<-matrix(0, nrow=nreps, ncol=1) for(i in 1:nreps) { sample <- rnorm(nObs, 2, 2) average[i] = mean(sample) } mean(average) var(average) hist(average) nreps = 1000 nObs= 100 z<-matrix(0, nrow=nreps, ncol=1) for(i in 1:nreps) { sample <- rnorm(nObs, 2, 2) z[i] =sqrt(nObs)*( mean(sample) - 2 )/ 2 } mean(z) var(z) hist(z) nreps = 1000 nObs= 1000 z<-matrix(0, nrow=nreps, ncol=1) for(i in 1:nreps) { sample <- rnorm(nObs, 2, 2) z[i] =sqrt(nObs)*( mean(sample) - 2 )/ 2 } mean(z) var(z) hist(z)...
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## This note was uploaded on 02/02/2010 for the course MGRL 194 taught by Professor Kirkpatrick during the Spring '08 term at Vanderbilt.

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econstatproj1 - histogram is more like a normal graph,...

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