Category:R ref: http://www.cyclismo.org/tutorial/R/confidence.html
- Calculating a 95% Confidence Interval from a t Distribution
values = iris$Sepal.Length n = length(values) m = mean(values) s = sd(values) error = qt(0.975,df=n-1)*s/sqrt(n) lower_limit = m - error upper_limit = m + error plot(density(values)) abline( v = c(lower_limit, upper_limit), col="red" )
- Calculating a 95% Confidence Interval from a Normal Distribution
values = iris$Sepal.Length n = length(values) m = mean(values) s = sd(values) error = qnorm(0.975)*s/sqrt(n) lower_limit = m - error upper_limit = m + error plot(density(values)) abline( v = c(lower_limit, upper_limit), col="red" )
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How to Calculate Confidence Interval from a Normal distribution
95% C. I. = M ± (1.96 * SE)
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How to plot Error Bars
bardata <- 1:3 # create some data bar <- barplot(bardata, ylim = c(0,4)) # store location of bars in bar, and # plot the barplot. ylim is used to # make room for the error bar later error <- c(.2,.4,.7) # create imaginary standard error arrows(bar, bardata + error, bar, bardata - error, length = 0.10, # width of the arrowhead angle = 90, # angle of the arrowhead code = 3 # arrowhead in both ends )
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