We get a binomial distribution by tossing a coin a fixed number of times. Also, let as coin may not be fair, then. In Graph 2, Graph seems to be incorrect, Kindly Confirm. As, it should be equal to 1: In continuous random variable definition condition 3, should it be: The expectation is a probability-weighted average. For CDF, it would be a step graph as shown in the figure.

You are commenting using your Twitter account. For a probability function: This was pointed out by sir in class as well. So cdf would be i. This satisfies first condition as and also:

Thanks for pointing out this mistake Like Like.

# P&S Lecture No. 4: Probability Functions and Continuous Random Variable – Theory at ITU

The question here is whether a and b can be equal or not? For a random variable X: So, this is a valid probability function. Yes they can be, and in that case will be equal to zero. As, it should be equal to 1: It is used when we have a fixed number of independent trials each with two possible outcomes success, failure and the probability of success is known. You are commenting using your Twitter account. The first range I considered is and the second is. So, CDF not probability is same throughout the interval because for any value of ‘x’ within the defined intervals, bomework takes same values CDF is calculated for values of ‘X’, the random variable and not ‘x’so the cumulative sum remains same.

I will update both graphs. The formula sayswhich means in 3.

# Cdf ps homework | Birmy EducationBirmy Education

So, the final equation is: It is what you would expect the outcome of an experiment to be on average. Let X is our random variable: Constructing a probability distribution for random variable. Please log in using one of these methods to post your comment: In it, the probability of any point is same i. How and why is the probability same within the whole interval?

For a non-uniform probabilities. PDF will always be discrete i. Discrete Probability Function Examples homfwork.

## Cdf ps homework

You have pointed it out right. You are commenting using your Facebook account.

Also, let as coin may not be fair, then Here is called parameter of the distribution. Here X indicates number of tosses to get a head.

## Cdf ps homework | Birmy Education

Published by Anas Muhammad. Expectation of Random Variables.

Yes, you are right. For checking whether a given probability function is a valid continuous probability function or not we have to check for above-mentioned conditions.

Here is called parameter of the distribution. Probability Function For a random variable X: Notify me of new comments via email.

For CDF, it would be a step graph as shown in the figure. If I select a number in the rangee.