presentation

• Assignment for Developing Data Products course, Coursera
• Implement the idea of Central Limit Theorem
• Please visit this web application for more detail https://i39vi4-thiha-naung.shinyapps.io/Central-Limit-Theorem-for-developing-data-product-course/?_ga=2.154240696.1021205865.1643264212-870001335.1643264212
• can choose 3 distributions, normal, binomial and poission
• can choose parameters to create your custom distribution such as mean, probability, lambda
• can also create your custom sampling distribution by choosing the sample size and sampling frequency

Example

• For normal distribution

x <- rnorm(10, 100, 1) # numbers in distribution, mean, standard deviation
x

 [1] 100.28190 100.24831 100.57220 101.89139  99.60102  98.79762  99.53777
 [8] 100.00382  99.75911  99.41948

• For binomial distribution

x <- rbinom(10, 5, 0.5) # numbers of distribution, size, probability
x

 [1] 2 2 4 4 2 1 4 3 3 2

• For poisson distribution

x <- rpois(10, 4) # numbers of distribution, lambda
x

 [1]  4  1 10  5  1  4  5  7  4  0

• You can choose all that parameters

x    <- rnorm(1000, 0, 1) # numbers of sample, mean, sd
results <- NA
for (i in c(1:10)){ # for loop for number-frequency times that you choose
    s <- sample(x, 10, replace=TRUE) # size of the data that you want to sampling
    results[i] <- mean(s) # calculate the mean and append the result to results 
}

results

 [1]  0.3406001  0.5451265  0.2838471  0.1782119 -0.5091241  0.2152859
 [7] -0.1953038 -0.3199212  0.1879391 -0.3135394