Probability And: Mathematical Statistics Theory Applications And Practice In R

# Hypothesis test example (one-sample t-test) output$testResults <- renderPrint({ if(dist == "Normal") { t.test(data, mu = input$mean) # test against true mean -> should fail to reject } else { t.test(data, mu = mean(data)) # dummy example } }) }) }

if(dist == "Normal") { data <- rnorm(n, input$mean, input$sd) theory_curve <- function(x) dnorm(x, input$mean, input$sd) } else if(dist == "Binomial") { data <- rbinom(n, input$size, input$prob) theory_curve <- function(x) dbinom(x, input$size, input$prob) } else if(dist == "Poisson") { data <- rpois(n, input$lambda) theory_curve <- function(x) dpois(x, input$lambda) } else { data <- rexp(n, input$rate) theory_curve <- function(x) dexp(x, input$rate) } input$sd) theory_curve &lt

# Plot 2: sampling distribution of the mean (simulated) output$samplingPlot <- renderPlot({ means <- replicate(1000, mean({ if(dist == "Normal") rnorm(n, input$mean, input$sd) else if(dist == "Binomial") rbinom(n, input$size, input$prob) else if(dist == "Poisson") rpois(n, input$lambda) else rexp(n, input$rate) })) df_means <- data.frame(mean = means) ggplot(df_means, aes(x = mean)) + geom_histogram(aes(y = after_stat(density)), bins = 30, fill = "orange", alpha = 0.6) + stat_function(fun = dnorm, args = list(mean = mean(means), sd = sd(means)), color = "red") + labs(title = "Sampling Distribution of the Mean (CLT in action)", x = "Sample mean") }) - function(x) dnorm(x