mba 522 Normal_approximation_to_Binomial

mba 522 Normal_approximation_to_Binomial - Normal...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
Normal approximation to Binomial De Moivre-Laplace limit theorem states that "for large n", the standard normal distribution approximates the binomial distribution (even though normal is continuous and binomial is discrete). Let's say you have a big box with many white and black balls in it. The black balls comprise 20% of the balls in the box. You can have a binomial trial started. Success is defined as a black ball drawn, and we know p=0.2 is the probability of success. Let's say the number of trials is 1000. That means the n is large. For each sample of 1000, count the number of black balls in the sample, and calculate the percentage of black balls in that sample. Let us say the first sample results in 28% black balls. On a horizontal axis, mark an X above a point that is marked .28. Return the balls to the box, mix them well, and take another sample of n=1000. Count the black balls in that sample, compute the percent of black balls, and plot it on the horizontal axis. Let us say it is 15%. Mark an X above the point that says 0.5 on the horizontal axis.
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 11/13/2011 for the course MBA 522 taught by Professor Nabavi during the Spring '08 term at Bellevue.

Ask a homework question - tutors are online