This is an attempt to illustrate the Law of Large No in stastical Analysis and is defined as Follows

• The theorm states that for large no of observations the probability of ocuurence of one event equals the theoritical probability

• In other words the theorm tells us that in the longer run practically the probability of happening of each of the equally likely events becomes almost the same

• As per the theorm stated the mean of the observation tends to reach towards theoritical true mean as the observations are increased

• This will also effect the plot and hence you should expect a more and more uniform plot with the increasing observations when sampled an equally like events

• To illustrate the law of large no, here i have simulated the coin Toss Experiment . The no of observaton can be adjusted using the slider

• More specifically the coin toss experiment is simulated using the Bernoulli Distribution as the chance of happening of each event does not decrease or increase and is fairly independant in each Toss

head(rbinom(500,size=1,prob = 0.5),10) 

##  [1] 0 0 1 1 0 1 0 0 1 0

• The function allows us me to do 500 simulation of Head and Tails each having the probabilty of occuring as 0.5

• Here I am printing only first 10 Observations

• The 500 specifically is replaced by input$range function to give the user a choice