How Does Probability Affect the Total Number of Coupons a Customer Can Expect?

In summary, the conversation discusses a math problem involving a sale at a shopping mall where new customers receive 10 coupons and have a chance to win 10 more coupons by spinning a fortune wheel. The average number of coupons a customer can make is computed using a model with a given chance of winning on the wheel. The question arises when there is no limit on the number of times a customer can spin the wheel, leading to a potentially infinite number of coupons. The conversation ends with a suggestion to find a formula for the expected number of coupons after N steps and take the limit as N approaches infinity.
  • #1
KFC
488
4
Hi all,
I am solving a practical math problem. There is a sale in one of the shopping mall in my town. The mall gives 10 coupons to a new customer. The customer could use one coupon at a time and when it is used, one could spin a fortune wheel to win more 10 more coupons. If one doesn't win additional coupons, he or she will get a chance next time using another coupon or no more drawing will be. So one customer will have not more than 20 coupons. I want to ask how many coupons on average a customer could make. Let's assume the customer use one coupon per day and the promotion activity going on for long enough for the customer to use all coupons. To setup a model, I assume there is ##p## chance to win the fortune wheel. At the beginning, a customer will get ##N=10## coupons, for the first time he/she uses the coupons, he/she has ##p## chance to win 10 more coupons and get ##q=1-p## chance to lose. If customer win additional 10 coupons, it could be when he/she use the kth coupon. So we need to consider all possible chance that the customer play and win the fortune wheel when using 1st, 2nd, 3rd ... coupon. The average number of coupons is computed as follows (here p is 0.001)

##
10 + 10 * (p + qp + q^2p + q^3 p + q^4 p + q^5p + \cdots + q^{10}p)
##

the sum in the parenthesis is said to be win at the first time or win at second time or win at third time, etc. I am not quite confident on this but running a computer simulation seems to get a result close to that given by above expression. Now comes to my question. The computation is straightforward when only ONE drawing is allowed. but what happen if there is no limit on number of drawing, i.e. customers will get a chance to win additional 10 coupons every time using a coupon. I find it pretty complicate because every time additional coupons won, it will have more chance to win more, it seems that there will be infinite terms to take care of. Any idea?
 
Physics news on Phys.org
  • #3
To solve this, get a formula for the expected number of coupons after N steps, then take the limit as N goes to infinity.

Here's a start:

N=1 gives 10
N=2 gives ##10+10p##
N=3 gives ##10+10p(1+10p)##
N=3 gives ##10+10p(1+10p(1+10p))##

Do you see a pattern forming?

Can you express this in a neat form that allows the limit to be seen as a standard infinite series?

Hint: the expectation does not exist ('is infinite') if ##10p\geq 1##.
 

Related to How Does Probability Affect the Total Number of Coupons a Customer Can Expect?

1. What is binomial statistics?

Binomial statistics is a branch of statistics that deals with the analysis of data that is divided into two categories, often referred to as "success" and "failure." It involves calculating the probability of obtaining a certain number of successes in a fixed number of trials, given a specific probability of success for each trial.

2. What is a binomial distribution?

A binomial distribution is a probability distribution that describes the possible outcomes of a binomial experiment. It is often represented as a histogram or a bar graph, with the number of successes on the x-axis and the probability of obtaining that number of successes on the y-axis.

3. What is the formula for calculating binomial probability?

The formula for calculating binomial probability is P(x) = (nCx)(px)(qn-x), where P(x) is the probability of obtaining x successes, n is the total number of trials, p is the probability of success on each trial, and q is the probability of failure on each trial (q = 1-p).

4. What are some real-world applications of binomial statistics?

Binomial statistics can be applied to a wide range of scenarios, including quality control in manufacturing, polling and opinion surveys, medical studies, and genetics research. It can also be used to analyze the success rates of marketing campaigns or the outcomes of sports games.

5. What is the difference between binomial and normal distribution?

The main difference between binomial and normal distribution is that binomial distribution is used to analyze discrete data with two possible outcomes, while normal distribution is used for continuous data with a wide range of possible outcomes. Additionally, binomial distribution is skewed and has a limited range, while normal distribution is symmetrical and can have a wide range of values.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
986
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
4K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
4
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
431
Replies
9
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
2K
Back
Top