Confidence Interval for Mu in a Log normal Distributions in R -


Suppose that our parameters are a random sample of size n = 8 with an unusual distribution with mu and sigma, suppose

Since this is a small sample, I am using confidence interval from a non-general population. I ran a simulation to determine the true (simulated) CI in a 90% T-CI in which Mu = 1 and sigma = 1.5

My problem is that my code follows normal distribution below And it needs to be an abnormal distribution. I know that the Ranomum has to be transformer so that random variables can come from log distribution. But I need to change mu and sigma. In normal distribution, mu and sigma are not equal in log delivery.

Log in delivery = exp (μ + 1/2 σ ^ 2) in mu. And sigma XP (2 (μ + sigma ^ 2)) - Exp 2 (μ + sigma ^ 2)

I'm just confused by how I incorporated these two equations into my code Can i

BTW- If you do not anticipate already, then I am very new to R. Any help would be appreciated! 

  MC < - Number of samples to simulate 10000 # result & lt; - c (1: MC) mu & lt; - 1 sigma & lt; - 1.5 n & lt; - 8; # Sample size alpha & lt; - 0.1 # Nominal confidence level 100 (1-alpha) percent t_criticalValue & lt; For qt (p = (1-alpha / 2), df = (n-1)) (i in 1: mc) {mySample & lt; - rlnorm (n = n, mean = mu, sd = sigma) lowerCL & lt; - Mean (mySample) -t_criticalValue * sd (mySample) / sqrt (n) Upper CL & lt; - Mean (mySample) + T_criticalValue * sd (mySample) / sqrt (n) Results [i] & lt; - ((Lower CL & amp; lt; mu) & amp; (mu & lt; Upper CL)) Simulated Confidential Level & lt; - Mean (result)

Edit: So I tried to replace Mu and SD with my related sources ...

(mu = exp (μ + 1/2 σ2) sigma = XP (2μ + σ2) (exp (Σ2) - 1)

And I got the 5000 simulated confidence level.

Here are some sample data samples: 

  (x <- rlnorm (8, 1, 2)

Your definition of important value was correct : 

  n <- length (x) alpha & lt; - 0.1 t_critical_value qt (1 > < There is a utility function in the 
 
  Ggplot2  package that creates a plot and calculates standard errors. In this case, you can find it for  mu  Applying to your logs of data and its confidence interval. 
 
  Library (ggplot2) mean_se (log (x), T_cratical_value) ## y ymin ymax ## 1 1.088481 -0.006944755 2.183907

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