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# for confidence Interval when given a proportion,number of population and the confidence level
confidenceInterval=function(p,n,confidenceLevel){
pn=p/n
q=1-pn
alpha=1-confidenceLevel
me=abs(qnorm(alpha/2))*sqrt(pn*q/n)
CI=p+me
CI2=p-me
print(CI)
print(CI2)
}
# To calculate the confidence Interval proportions when proportion,number of population and the confidence level are given
confprops=function(proportionSize,populationSize,confidenceLevel){
pproportion=proportionSize/populationSize
qproportion=1-pproportion
alpha=1-confidenceLevel
marginerror=abs(qnorm(alpha/2))*sqrt(pproportion*qproportion/n)
CI=pproportion+marginerror
CI2=pproportion-marginerror
print(CI)
CI2
}
# To Calculate the margin error when given proportion size,poputlation size and confidence level
marginOfError=function(proportionSize,populationSize,confidenceLevel){
p=proportionSize/populationSize
q=1-p
alpha=1-confidenceLevel
zscore=abs(qnorm(alpha/2))
marginOfError=zscore*sqrt(p*q/populationSize)
marginOfError
}
To calculate the population size when margin error, confidence level and standard deviation is given.
size=function(marginOfError,confidenceLevel,standardDeviation){
alpha=1-confidenceLevel
zscore=abs(qnorm(alpha/2))
size=(zscore*standardDeviation/marginOfError)^2
size
}
confidenceLevel=99/100
alpha=1-confidenceLevel
half_alpha=alpha/2
criticalValue=abs(qnorm(half_alpha))
print(criticalValue)
> confidenceLevel=99/100
> alpha=1-confidenceLevel
> half_alpha=alpha/2
> criticalValue=abs(qnorm(half_alpha))
> print(criticalValue)
[1] 2.575829
# To calculate the confidence Interval proportions when proportion,number of population and the confidence level are given
confprops=function(proportionSize,populationSize,confidenceLevel){
pproportion=proportionSize/populationSize
qproportion=1-pproportion
alpha=1-confidenceLevel
marginerror=abs(qnorm(alpha/2))*sqrt(pproportion*qproportion/populationSize)
CI=pproportion+marginerror
CI2=pproportion-marginerror
print(CI)
CI2
}
confprops(500,1500,0.95)
confprops(250,1500,0.95)
confprops(1000,1500,0.95)
> > confprops=function(proportionSize,populationSize,confidenceLevel){
+ pproportion=proportionSize/populationSize
+ qproportion=1-pproportion
+ alpha=1-confidenceLevel
+ marginerror=abs(qnorm(alpha/2))*sqrt(pproportion*qproportion/populationSize)
+ CI=pproportion+marginerror
+ CI2=pproportion-marginerror
+ print(CI)
+ CI2
+ }
> confprops(500,1500,0.95)
[1] 0.3571893
[1] 0.3094774
> confprops(250,1500,0.95)
[1] 0.1855264
[1] 0.1478069
> confprops(1000,1500,0.95)
[1] 0.6905226
[1] 0.6428107
p=0.7
q=1-p
n=2500
standardError=sqrt(p*q/n)
print(standardError)
p=0.7
> q=1-p
> n=2500
> standardError=sqrt(p*q/n)
> print(standardError)
[1] 0.009165151
size,poputlation size and confidence level
marginOfError=function(proportionSize,populationSize,confidenceLevel){
p=proportionSize/populationSize
q=1-p
alpha=1-confidenceLevel
zscore=abs(qnorm(alpha/2))
marginOfError=zscore*sqrt(p*q/populationSize)
marginOfError
}
marginOfError(1383,2305,0.95)
> marginOfError=function(proportionSize,populationSize,confidenceLevel){
+ p=proportionSize/populationSize
+ q=1-p
+ alpha=1-confidenceLevel
+ zscore=abs(qnorm(alpha/2))
+ marginOfError=zscore*sqrt(p*q/populationSize)
+ marginOfError
+ }
> marginOfError(1383,2305,0.95)
[1] 0.01999946
size=function(marginOfError,confidenceLevel,standardDeviation){
alpha=1-confidenceLevel
zscore=abs(qnorm(alpha/2))
size=(zscore*standardDeviation/marginOfError)^2
size
}
size(0.045,0.95,2)
> size=function(marginOfError,confidenceLevel,standardDeviation){
+ alpha=1-confidenceLevel
+ zscore=abs(qnorm(alpha/2))
+ size=(zscore*standardDeviation/marginOfError)^2
+ size
+ }
> size(0.045,0.95,2)
[1] 7588.067
07045225718
if you have any doubt or correction on this. Enjoy coding!