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Combinatorial Probabilities Cheat Sheet, Cheat Sheet of Statistics

Key concept and Formulas of Combinatorial Probabilities

Typology: Cheat Sheet

2019/2020

Uploaded on 11/27/2020

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Math 408, Actuarial Statistics I A.J. Hildebrand
Combinatorial Probabilities
Key concepts
Permutation: arrangement in some order.
Ordered versus unordered samples: In ordered samples, the order of the elements
in the sample matters; e.g., digits in a phone number, or the letters in a word. In
unordered samples the order of the elements is irrelevant; e.g., elements in a subset, or
lottery numbers.
Samples with replacement versus samples without replacement: In the first
case, repetition of the same element is allowed (e.g., numbers in a license plate); in the
second, repetition not allowed (as in a lottery drawing—once a number has been drawn,
it cannot be drawn again).
Formulas
Number of permutations of nobjects: n!
Number of ordered samples of size r,with replacement, from nobjects: nr
Number of ordered samples of size r,without replacement, from nobjects:
n(n1) · · · (nr+ 1) = n!
(nr)! =nPr.
Number of unordered samples of size r,without replacement, from a set of nobjects
(= number of subsets of size rfrom a set of nelements) (combinations):
n
r=nPr
r!=n!
r!(nr)! =n(n1) . . . (nr+ 1)
r!.
(See back of page for properties of these binomial coefficients.)
Number of subsets of a set of nelements: 2n
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Math 408, Actuarial Statistics I A.J. Hildebrand

Combinatorial Probabilities

Key concepts

  • Permutation: arrangement in some order.
  • Ordered versus unordered samples: In ordered samples, the order of the elements in the sample matters; e.g., digits in a phone number, or the letters in a word. In unordered samples the order of the elements is irrelevant; e.g., elements in a subset, or lottery numbers.
  • Samples with replacement versus samples without replacement: In the first case, repetition of the same element is allowed (e.g., numbers in a license plate); in the second, repetition not allowed (as in a lottery drawing—once a number has been drawn, it cannot be drawn again).

Formulas

  • Number of permutations of n objects: n!
  • Number of ordered samples of size r, with replacement, from n objects: nr
  • Number of ordered samples of size r, without replacement, from n objects:

n(n − 1) · · · (n − r + 1) = n! (n − r)! = (^) nPr.

  • Number of unordered samples of size r, without replacement, from a set of n objects (= number of subsets of size r from a set of n elements) (combinations): ( n r

= n Pr r!

n! r!(n − r)!

n(n − 1)... (n − r + 1) r!

(See back of page for properties of these binomial coefficients.)

  • Number of subsets of a set of n elements: 2n

Math 408, Actuarial Statistics I A.J. Hildebrand

Binomial coefficients

  • Definition: For n = 1, 2 ,... and k = 0, 1 ,... , n,

n k

n! k!(n − k)!

(Note that, by definition, 0! = 1.)

  • Alternate notations: (^) nCk or C(n, k)
  • Alternate definition:

n k

n(n − 1)... (n − k + 1) k!

(This version is convenient for hand-calculating binomial coefficients.)

  • Symmetry property:

n k

n n − k

  • Special cases:

n 0

n n

n 1

n n − 1

= n

  • Binomial Theorem: (x + y)n^ =

∑^ n

k=

n k

xkyn−k

  • Binomial Theorem, special case:

∑^ n

k=

n k

pk(1 − p)n−k^ = 1

  • Combinatorial Interpretations:

n k

represents

  1. the number of ways to select k objects out of n given objects (in the sense of unordered samples without replacement);
  2. the number of k-element subsets of an n-element set;
  3. the number of n-letter HT sequences with exactly k H’s and n − k T’s.
  • Binomial distribution: Given a positive integer n and a number p with 0 < p < 1, the binomial distribution b(n, p) is the distribution with density (p.m.f.) f (k) =

n k

pk(1 − p)n−k, for k = 0, 1 ,... , n.