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Probability Theory: Calculating Probabilities and Counting Sample Points in STAT 3401, Exams of Probability and Statistics

The sample-point method for calculating probabilities and tools for counting sample points in the context of stat 3401, introductory probability theory. Topics covered include calculating probabilities using the sample-point method, counting sample points using the mn rule and permutations, and understanding the difference between permutations and combinations. Examples include calculating probabilities of selecting applicants, tossing dice, and the birthday problem.

Typology: Exams

Pre 2010

Uploaded on 09/17/2009

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STAT 3401, Intro. Prob. Theory 11/21 Jaimie Kwon
1/6/2005
2.5 Calculating the probability of an event: the sample-point
method
To find the probability of an event by the sample-point method, use the following steps:
o Define the experiment and clearly determine how to describe one simple event
o List the simple events associated with the experiment and test each to make certain
that it cannot be decomposed. (this defines the sample space S)
o Assign reasonable probabilities to the sample points in S, making certain that P(Ei)0
and ΣP(Ei)=1.
o Define the event of interest, A, as a specific collection of sample points. (A sample point
is in A if A occurs when the sample point occurs. Test all sample points in S to identify
those in A.)
o Find P(A) by summing the probabilities of the sample points in A.
Example 2.2. An employer “randomly” selects two applicants for a job from five employer,
with competence 1 (best) to 5 (worst). Evaluate:
P(the employer selects the best and one of the two poorest applicants)=
P(the employer selects at least one of the two bests)=
Example 2.3. In a balanced coin tossing, compute P(exactly 2 of 3 tosses are heads)
Example 2.4. When A and B plays tennis, the odds are two to one that A wins. If A and B
plays two match, evaluate P(A wins at least one match).
The method is direct and powerful (a bulldozer approach)
Difficulties:
o Prone to human errors: forget some sample points in S
o Many sample spaces contain a very large number of sample points
Detours:
o Many sample spaces generated by experimental data contain subset of sample points
that are equiprobable.
o Use a computer to list the large number of sample points and sum their probabilities
o The mathematical theory of counting, called “combinatorial analysis”
HW: some questions from exercises 2.18~ 2.26
Keywords: the sample-point method
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2.5 Calculating the probability of an event: the sample-point

method

  • To find the probability of an event by the sample-point method, use the following steps: o Define the experiment and clearly determine how to describe one simple event o List the simple events associated with the experiment and test each to make certain that it cannot be decomposed. (this defines the sample space S) o Assign reasonable probabilities to the sample points in S, making certain that P(E (^) i )≥ 0 and ΣP(Ei)=1. o Define the event of interest, A, as a specific collection of sample points. (A sample point is in A if A occurs when the sample point occurs. Test all sample points in S to identify those in A.) o Find P(A) by summing the probabilities of the sample points in A.
  • Example 2.2. An employer “randomly” selects two applicants for a job from five employer, with competence 1 (best) to 5 (worst). Evaluate: P(the employer selects the best and one of the two poorest applicants)= P(the employer selects at least one of the two bests)=
  • Example 2.3. In a balanced coin tossing, compute P(exactly 2 of 3 tosses are heads)
  • Example 2.4. When A and B plays tennis, the odds are two to one that A wins. If A and B plays two match, evaluate P(A wins at least one match).
  • The method is direct and powerful (a bulldozer approach)
  • Difficulties: o Prone to human errors: forget some sample points in S o Many sample spaces contain a very large number of sample points
  • Detours: o Many sample spaces generated by experimental data contain subset of sample points that are equiprobable. o Use a computer to list the large number of sample points and sum their probabilities o The mathematical theory of counting, called “combinatorial analysis”
  • HW: some questions from exercises 2.18~ 2.
  • Keywords: the sample-point method

2.6 Tools for counting sample points

  • Aim: count the total number of sample points in the sample space S and in an event of interest o If a sample space contains N equiprobable sample points and an event A contains exactly n (^) a sample points, P(A)=n (^) a/N.
  • Theorem 2.1. With m elements a 1 , a 2 , …,a m and n elements b 1 , b 2 , …,b n it is possible to

form mn(=m×n) pairs containing one element from each group.

o The “mn rule” can be extended to any number of sets. Given three sets of elements a 1 , a 2 , …,a m , b 1 , b 2 , …,b n and c 1 , c 2 , …,c p, the number of distinct triplets (a i ,b j ,c k) containing one element from each set is equal to mnp. (Why?)

  • Example 2.5. (Tossing two dice) In tossing a pair of dice and observing the numbers on the upper fces, find the # of sample points in the sample space S.
  • Example 2.6. Redo 2.3 (coin tossing) using the mn rule.
  • Example 2.7. (Birthday problem) Consider an experiment that consists of recording the birthday for each of 20 randomly selected persons. Ignoring leap years and assuming that there are only 365 possible distinct birthdays, find the # of points in the sample space S for this experiment. If we assume that each of the possible sets of birthdays is equiprobable, what’s the probability that each person in the 20 has a different birthday? o A sample point is an ordered sequence of 20 numbers, each corresponding to the birthday of a person from 365 days. o The mn rule tells us that there are (365) 20 such twenty-tuples. The sample space S contains N=(365) 20 sample points. o It’s not feasible to list all the sample points o But if we assume them equiprobable, P(E (^) i )=1/(365) 20 for each sample event. o Let A be the event that each person has a different birthday. o P(A)=n (^) a/N where n (^) a=(# of sample points in A) = |A| o A={20-tuples such that no two positions contain the same number} o n (^) a=365 × 364 ×…× 356 o P(A) =.
  • The sample points associated with an experience often can be represented symbolically as a sequence of numbers or symbols. o In some instances, the total # of sample points equals the # of distinct ways that the respective symbols can be arranged in sequence

o The terms are called “binomial coefficients” because they occur in the “binomial

expansion” ( ) ∑

=

− 

 

  • =^ ni

n (^) xniyi r

n x y 0

  • Example 2.11. # of ways of selecting two applicants out of five (cf. example 2.2)
  • Example 2.12. (continuation of 2.11) Compute # of sample points in A={exactly one of the two best applicants is selected} and P(A)
  • Example 2.13. When placing n orders from M distributors, for a particular distributor say I, compute P(I gets exactly k orders).
  • Warning: Combinatorial analysis is a huge area!
  • HW: some questions from exercises 2.27~2.
  • Keywords: mn rule; permutation; partitioning; combinations; multinomial/binomial expansions