How normal distribution is limiting case of binomial distribution?

It means that for any binomial distribution the distribution looks more and more like the normal distribution as you increase the number of cases. In the limit, for an infinitely large number of cases, the binomial is indistinguishable from the normal.

What is the limit of binomial distribution?

We know that as n→∞, the binomial distribution B(n,p), with fixed p, after appropriate normalization, converges to a normal distribution. If p=c/n for some constant c, then it converges to the Poisson distribution.

What is the relationship between normal distribution and binomial distribution?

Normal distribution describes continuous data which have a symmetric distribution, with a characteristic ‘bell’ shape. Binomial distribution describes the distribution of binary data from a finite sample. Thus it gives the probability of getting r events out of n trials.

What is the standard deviation of a normal distribution?

A normal distribution is the proper term for a probability bell curve. In a normal distribution the mean is zero and the standard deviation is 1. It has zero skew and a kurtosis of 3. Normal distributions are symmetrical, but not all symmetrical distributions are normal.

Why is binomial distribution important?

The binomial distribution model allows us to compute the probability of observing a specified number of “successes” when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure.

Why is normal distribution not good for financial data?

Give a reason why a normal distribution, with this mean and standard deviation, would not give a good approximation to the distribution of marks. My answer: Since the standard deviation is quite large (=15.2), the normal curve will disperse wildly. Hence, it is not a good approximation.