First, I have createt a Shiny application and deployed it on Rstudio’s servers. Second, I have used Rstudio Presenter to prepare a reproducible pitch presentation about your application.

The app developed for the first part of the assignment is avalilable at: https://osreama.shinyapps.io/Week4_Assignment/

Source code for ui.R and server.R files are available on the GitHub at: https://github.com/osreama1/week4_assigment

POISSON DISTRIBUTION is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event. The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.

The positive real number λ is equal to the expected value of X and also to its variance.

• mean=λ
  
• standard deviation=sqrt(λ)

Define Poisson distribution with lambdas : 2,10 and 20

As can be seen in the plots shown, when we increase the lambda value, the poisson distribution tends to be a normal distribution.

The same effect occurs with the number of samples. As we increase the number of samples, the distribution tends to be a normal distrubution as well.