How many sandwich combinations can you create with different garnish options?

In summary, the problem involves choosing one of six breads, one of four cheeses, one of four meats, and up to 12 garnishes. In part A, the number of possible sandwiches without garnishes is calculated to be 96. In part B, the number of ways to pick garnishes, with the restriction of not choosing the same one twice, is calculated using the combination formula to be 2^12 or 4096.
  • #1
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Here is the problem:


You have a sandwich shop. You can choose one of 6 different breads, 1 of four different cheeses, one of four different meats, and you can choose up to 12 garnishes, out of 0 to 12 garnishes.


Here is my solution so far.

In part A I calculated the amount of possible sandwiches without garnishes to be 96.


6*4^2 = 96


In part B i must calculated the following:

One bread, one meat, one cheese, and from 0 to 12 garnishes? (Remember there are 12 different choices for the garnish but you cannot choose the same garnish twice, so for each one of those 12 there are different possibilities.)

My first idea is to use the combination formula

nCr = n!/( r! (n-r)!)

I hesitate because I am confused of how to use it in respect to "from 0 to 12 garnishes." with there also being 12 garnish choices.

Thank you very much for clearing up some confusion.
 
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  • #2
Solved. Thanks for the help anyway. :) 2^12
 
  • #3
You could use combinations. The number of ways to pick garnishes is

[tex]\sum_{r = 0}^{12} \binom{12}{r}[/tex]

which simplifies to

[tex]\sum_{r = 0}^{12} \binom{12}{r}
= \sum_{r = 0}^{12} \binom{12}{r} (1)^r (1)^{12 - r}
= (1 + 1)^{12}
= 2^{12}
[/tex]
 

What is combination probability?

Combination probability is a mathematical concept that calculates the likelihood of an outcome from a set of possible outcomes, where order does not matter and repetitions are not allowed. It is commonly used in statistics, probability, and data analysis to determine the probability of a specific outcome in a given situation.

How is combination probability different from permutation probability?

The main difference between combination probability and permutation probability is that combination probability considers the number of ways to choose a subset of objects from a larger set, while permutation probability considers the number of ways to arrange a set of objects in a specific order. In combination probability, the order of the chosen objects does not matter, while in permutation probability, the order is important.

What is the formula for calculating combination probability?

The formula for combination probability is nCr = n! / (r!(n-r)!), where n is the total number of objects and r is the number of objects being chosen. This is also known as the "choose" function, as it represents the number of ways to choose r objects from a set of n objects.

How is combination probability used in real life?

Combination probability is used in various real-life situations, such as in gambling, sports betting, and market research. It can also be used to calculate the probability of winning a lottery or the chances of getting a certain hand in a card game. In market research, combination probability is used to predict consumer behavior and make informed decisions based on the likelihood of certain outcomes.

What are some common misconceptions about combination probability?

One common misconception about combination probability is that it only applies to a limited number of objects or events. In reality, combination probability can be applied to any number of objects or events, as long as the criteria of order not mattering and repetitions not being allowed are met. Another misconception is that the "choose" function only applies to combinations of two objects, when in fact it can be used for any number of objects being chosen from a larger set.

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