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Glen_b
Glen_b
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For example, in R if you call the afcacf() function it plots a correlogrammcorrelogram by default, and draws a 95% confidence interval. Looking at the code, if you call plot(acf_object, ci.type="white"), you see:

qnorm((1 + ci)/2)/sqrt(x$n.used)

as upper limit for type white-noise. Can some one explain theory behind this method? Why do we get the qnorm of 1+0.95 and then divide by 2 and after that, divide by the number of observations?

For example, in R if you call the afc() function it plots a correlogramm by default, and draws a 95% confidence interval. Looking at the code, if you call plot(acf_object, ci.type="white"), you see:

qnorm((1 + ci)/2)/sqrt(x$n.used)

as upper limit for type white-noise. Can some one explain theory behind this method? Why do we get the qnorm of 1+0.95 and then divide by 2 and after that, divide by the number of observations?

For example, in R if you call the acf() function it plots a correlogram by default, and draws a 95% confidence interval. Looking at the code, if you call plot(acf_object, ci.type="white"), you see:

qnorm((1 + ci)/2)/sqrt(x$n.used)

as upper limit for type white-noise. Can some one explain theory behind this method? Why do we get the qnorm of 1+0.95 and then divide by 2 and after that, divide by the number of observations?

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gung - Reinstate Monica
gung - Reinstate Monica
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How R calculatesis the confidence interval when plotcalculated for the ACF function?

For example, in R if you call afc()the afc() function its plotit plots a correlogramm by default, and drawdraws a 95% confidence interval (ci=0.95). lookLooking at the code, if you call plot(acf_objectplot(acf_object, ci.type="white"),ci.type="white") you see:

qnorm((1 + ci)/2)/sqrt(x$n.used)

it isas upper limit for type white-noise, can. Can some one explain theory ofbehind this method? whyWhy do we get qnorthe qnorm of 1+0.95 and get /2then divide by 2 and after it /numberofobservations.that, divide by the number of observations?

How R calculates confidence interval when plot ACF?

if you call afc() function its plot correlogramm by default, and draw confidence interval (ci=0.95). look at code, if you call plot(acf_object,ci.type="white")

qnorm((1 + ci)/2)/sqrt(x$n.used)

it is upper limit for type white-noise, can some one explain theory of this method? why we get qnor of 1+0.95 and get /2 and after it /numberofobservations.

How is the confidence interval calculated for the ACF function?

For example, in R if you call the afc() function it plots a correlogramm by default, and draws a 95% confidence interval. Looking at the code, if you call plot(acf_object, ci.type="white"), you see:

qnorm((1 + ci)/2)/sqrt(x$n.used)

as upper limit for type white-noise. Can some one explain theory behind this method? Why do we get the qnorm of 1+0.95 and then divide by 2 and after that, divide by the number of observations?

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Nick Nikolaev
Nick Nikolaev
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How R calculates confidence interval when plot ACF?

if you call afc() function its plot correlogramm by default, and draw confidence interval (ci=0.95). look at code, if you call plot(acf_object,ci.type="white")

qnorm((1 + ci)/2)/sqrt(x$n.used)

it is upper limit for type white-noise, can some one explain theory of this method? why we get qnor of 1+0.95 and get /2 and after it /numberofobservations.