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- vehicleType <- c("suv", "suv", "minivan", "car", "suv", "suv", "car", "car", "car", "car", "minivan", "car", "truck", "car", "car", "car", "car", "car", "car", "car", "minivan", "car", "suv", "minivan", "car", "minivan", "suv", "suv", "suv", "car", "suv", "car", "car", "suv", "truck", "truck", "minivan", "suv", "car", "truck", "suv", "suv", "car", "car", "car", "car", "suv", "car", "car", "car", "suv", "car", "car", "car", "truck", "car", "car", "suv", "suv", "minivan", "suv", "car", "car", "car", "car", "car", "minivan", "suv", "car", "car", "suv", "minivan", "car", "car", "car", "minivan", "minivan", "minivan", "car", "truck", "car", "car", "car", "suv", "suv", "suv", "car", "suv", "suv", "car", "suv", "car", "minivan", "car", "car", "car", "car", "car", "car", "car")
- p_hat +/- z * sqrt(p_hat * (1-p_hat)/n)
- # Set CI alpha level (1-alpha/2)*100%
- alpha = 0.05
- # Load Data
- vehicleType = c("suv", "suv", "minivan", "car", "suv", "suv", "car", "car", "car", "car", "minivan", "car", "truck", "car", "car", "car", "car", "car", "car", "car", "minivan", "car", "suv", "minivan", "car", "minivan", "suv", "suv", "suv", "car", "suv", "car", "car", "suv", "truck", "truck", "minivan", "suv", "car", "truck", "suv", "suv", "car", "car", "car", "car", "suv", "car", "car", "car", "suv", "car", "car", "car", "truck", "car", "car", "suv", "suv", "minivan", "suv", "car", "car", "car", "car", "car", "minivan", "suv", "car", "car", "suv", "minivan", "car", "car", "car", "minivan", "minivan", "minivan", "car", "truck", "car", "car", "car", "suv", "suv", "suv", "car", "suv", "suv", "car", "suv", "car", "minivan", "car", "car", "car", "car", "car", "car", "car")
- # Convert from string to factor
- vehicleType = factor(vehicleType)
- # Find the number of obs
- n = length(vehicleType)
- # Find number of obs per type
- vtbreakdown = table(vehicleType)
- # Get the proportion
- p_hat = vtbreakdown['suv']/n
- # Calculate the critical z-score
- z = qnorm(1-alpha/2)
- # Compute the CI
- p_hat + c(-1,1)*z*sqrt(p_hat*(1-p_hat)/n)
- 0.1740293 0.3459707
- 0.26
- bp = function(x, lev, n = 1e3, alpha=0.05) {
- res = replicate(n, sum(sample(x, length(x), replace=TRUE) == lev)/length(x))
- return(list(mean=mean(res),
- `95% CI`=quantile(res, c(0.5*alpha,1-0.5*alpha))))
- }
- bp(vehicleType, "suv")
- $mean
- [1] 0.259628
- $`95% CI`
- 2.5% 97.5%
- 0.18 0.35
- library(binom)
- binom.confint(sum(vehicleType=="suv"), length(vehicleType))
- method x n mean lower upper
- 1 agresti-coull 26 100 0.2600000 0.1836007 0.3541561
- 2 asymptotic 26 100 0.2600000 0.1740293 0.3459707
- 3 bayes 26 100 0.2623762 0.1788095 0.3485750
- 4 cloglog 26 100 0.2600000 0.1787357 0.3485852
- 5 exact 26 100 0.2600000 0.1773944 0.3573121
- 6 logit 26 100 0.2600000 0.1835016 0.3545416
- 7 probit 26 100 0.2600000 0.1818365 0.3526030
- 8 profile 26 100 0.2600000 0.1808127 0.3513344
- 9 lrt 26 100 0.2600000 0.1808329 0.3513338
- 10 prop.test 26 100 0.2600000 0.1797427 0.3590222
- 11 wilson 26 100 0.2600000 0.1840470 0.3537099
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