Calculating Distribution Functions for Four Coin Toss Experiment

In summary, the experimental distribution function for this experiment is fj=nj/N, where nj is the number of counts for jth heads for N trials. The expected distribution for this experiment is given by C!/(C-xj)!(xj!)(2^C), where xj is the number of heads.
  • #1
GreyGus
23
0

Homework Statement


For this exercise, four coins are tossed 32 times and the number of heads are recorded for each toss. Each toss falls into one of the following macroscopic states; 0 heads, 1 heads, 2 heads, 3 heads and 4 heads. Suppose the 32 tosses result in the following outcome: 3,2,3,2,2,4,0,3,0,2,0,4,4,2,3,1,2,0,1,3,2,3,1,3,3,2,3,2,3,3,2 and 1. Your task is to count the number of times when 1 heads, 2 heads, ... appears, and to calculate the measured and expected distribution functions.
To calculate the measured distribution function, if nj is the number of counts for jth heads for N trials, then the experimental distribution function is fj=nj/N. For example, the number of counts with zero heads is 4 giving f0=4/32=0.125.
The expected distribution for such an experiment follows a binomial distribution function and is given by
C!/(C-xj)!(xj!)(2^C)
where C is the total number of coins, xj is the number of heads. Thus for the case of 0 heads, f0=4!(4−0)!0!2^4=1/16=0.0625.


Homework Equations


C!/(C-xj)!(xj!)(2^C)


The Attempt at a Solution


    • C
    • 4
    • 10
    • 11
    3
    • fj
    • 0.125
    • 0.3125
    • 0.34375
    • 0.09375
    • distribution
    • 0.25
    • 0.043945313
    • 0.080566406
    • #NUM!
    • Head number
    • 1
    • 2
    • 3
    • 4

For the last one the factorial is for a negative number for it doesn't work.
Please help. Any help would be greatly appreciated.
 
Last edited:
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  • #2
How are you getting the factorial of a negative number. C is 4 and xj runs from 0 to 4 so C-xj runs from 4 to 0. There are no negative numbers involved.
 
  • #3
GreyGus said:

Homework Equations


C!/(C-xj)!(xj!)(2^C)
That gives the probability of a toss having xj heads (or tails). Thus 0 heads has a probability of 1/16. If you toss the coins 32 times, the expected number of 0 head tosses is (1/16)(32) = 2. Compare that expected value with the actual number seen in a particular series of tosses.

(I'm not sure how you got a negative number anywhere.)
 
  • #4
I had a negative number because for the last one I applied the formula and got
(3!)/(3-4)!(4)!(2^3).

I might have posted the problem wrong, for that I apologize. But it's suppose to be four columns, one for the different C's I counted, fj's, expected distribution and the head number I'm suppose to count. Like if it's 2 ok, so how many 2's are there. So that's how I did it and got a negative number.
 
Last edited:
  • #5
GreyGus said:
I had a negative number because for the last one I applied the formula and got
(3!)/(3-4)!(4)!(2^3).
This makes no sense. Here you have C = 3, meaning you are flipping 3 coins. And you have xj = 4, meaning you are looking for 4 heads! :bugeye:

As far as I can see, you are always flipping four coins at a time, so C is always 4. xj varies from 0 to 4. No negatives.
 

Related to Calculating Distribution Functions for Four Coin Toss Experiment

1. What is a distribution function?

A distribution function, also known as a cumulative distribution function, is a mathematical function that shows the probability of a random variable taking on a value less than or equal to a given value. It is used to describe the probability distribution of a random variable.

2. How is a distribution function calculated?

A distribution function is calculated by taking the sum of the probabilities of all possible outcomes up to a given value. In the case of a coin toss experiment, the distribution function can be calculated by adding the probabilities of getting 0, 1, 2, 3, or 4 heads in four tosses.

3. What is the purpose of calculating distribution functions for a coin toss experiment?

The purpose of calculating distribution functions for a coin toss experiment is to understand the probability distribution of the outcomes. This can help in making predictions about the likelihood of getting a certain number of heads in a given number of coin tosses.

4. How does the number of tosses affect the distribution function in a coin toss experiment?

The number of tosses affects the distribution function in a coin toss experiment by changing the shape of the distribution. As the number of tosses increases, the distribution becomes more symmetrical and approaches a normal distribution.

5. Can distribution functions be used for other types of experiments?

Yes, distribution functions can be used for other types of experiments where there are discrete outcomes with known probabilities. Examples include rolling a dice, drawing cards from a deck, or spinning a roulette wheel.

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