Probability mass function

In summary, the probability mass function of the number of heads that can be obtained by rolling a fair die and then flipping a coin a number of times equal to the number rolled is (1/6)(1/2 + 1/4 + 1/8 + ... + 1/2^n). This can be obtained by considering the probability of rolling a certain number on the die and then flipping the coin a certain number of times to obtain a certain number of heads. The probability of rolling a certain number on the die is 1/6 and the probability of getting a certain number of heads in a certain number of flips is (1/2)^n.
  • #1
kingwinner
1,270
0

Homework Statement


Suppose we roll a fair die. Whatever number comes up, we toss a coin that many times. What is the probability mass function of the number of heads?


Homework Equations


N/A


The Attempt at a Solution


Let X=number of heads
Then I know that X can take on the values 0,1,2,3,4,5,6.
How to proceed from here I have no clue...


Could someone please explain? Any help would be appreciated!
 
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  • #2
Just direct computation. The probability of 0 is 0 because there is no "0" on a die. What is the probability of rolling a 1 on a die? Of a 2? etc.
 
  • #3
By the way, is this a "birvariate" distirbution? How can I define the other random variable?
 
  • #4
Could somebody please help? I am totally stuck...
 
  • #5
Start with the probability of rolling a one...what is that?
 
  • #6
Literally, see what the probability of getting 1 heads is by finding the probability you roll x on the die, and multiplying by the probability of getting 1 heads in x coin flips. Rinse and repeat
 
  • #7
gabbagabbahey said:
Start with the probability of rolling a one...what is that?
Uniform prob. of 1/6 for each number on the die
And the prob. of a head is 1/2
...but I am still having trouble combining these...
 
  • #8
Probability of 6 heads= 1/6 times (1/2)^6

Probabilty of 5 heads=...(no, it's not just 1/6 times (1/2)^5)
 
  • #9
Take it ones step at a time.

There is a 1/6 probability of rolling a "1" and then you flip the coin once which has probability 1/2 of getting a head: probability of getting 1 head this way is (1/6)(1/2)= 1/12.

There is a probability of 1/6 of rolling a 2 and then you flip the coin twice which has a probability 1/2 go getting one head and a probability 1/4 of getting two heads.
Probability of getting 1 head this way is 1/12 and probability of getting two heads is 1/24.

Do you see a pattern? If you roll an "n" on the coin you flip the coin n times. You have a probability of 1-(1/2)^n of getting a 1. Altogether the probability getting a one is (1/6)(1/2+ 1/4+ 1/8+ ...). What is the probability of getting two heads if you fllip a coin n times? What is the probability of rolling an "n"?
 

1. What is a probability mass function (PMF)?

A probability mass function, or PMF, is a mathematical function that assigns probabilities to discrete random variables. It represents the probability distribution of a discrete random variable, showing the probability of each possible outcome.

2. How is a PMF different from a probability density function (PDF)?

A PMF is used for discrete random variables, while a PDF is used for continuous random variables. In a PMF, the probabilities are represented as discrete values, while in a PDF, the probabilities are represented as a continuous curve.

3. What is the sum of all probabilities in a PMF?

The sum of all probabilities in a PMF must equal 1. This is because a PMF represents the probability distribution for all possible outcomes of a random variable, and the sum of all probabilities must account for all possible outcomes.

4. How is a PMF used in statistical analysis?

A PMF is used to calculate the likelihood of a certain outcome occurring in a sample or population. It can also be used to compare the probabilities of different outcomes, and to make predictions about future events based on past data.

5. What is the difference between a PMF and a cumulative distribution function (CDF)?

A PMF represents the probabilities of individual outcomes, while a CDF represents the probability of a random variable being less than or equal to a certain value. In other words, a CDF is the cumulative sum of probabilities in a PMF.

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