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Statistics 270 - Lecture 5. Last class: measures of spread and box-plots Last Day - Began Chapter 2 on probability. Section 2.1 These Notes – more Chapter 2…Section 2.2 and 2.3 Assignment 2: 2.8, 2.12, 2.18, 2.24, 2.30, 2.36, 2.40 Due: Friday, January 27 Suggested problems: 2.26, 2.28, 2.39.

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  • Last class: measures of spread and box-plots

  • Last Day - Began Chapter 2 on probability. Section 2.1

  • These Notes – more Chapter 2…Section 2.2 and 2.3

  • Assignment 2: 2.8, 2.12, 2.18, 2.24, 2.30, 2.36, 2.40

    • Due: Friday, January 27

  • Suggested problems: 2.26, 2.28, 2.39


Probability
Probability

  • Probability of an event is the long-term proportion of times the event would occur if the experiment is repeated many times

  • Read page 59-60 on Interpreting probability


Probability1
Probability

  • Probability of event, A is denoted P(A)

  • Axioms of Probability:

    • For any event, A,

    • P(S) = 1

    • If A1, A2, …, Akare mutually exclusive events,

  • These imply that


Discrete uniform distribution
Discrete Uniform Distribution

  • Sample space has k possible outcomes S={e1,e2,…,ek}

  • Each outcome is equally likely

  • P(ei)=

  • If A is a collection of distinct outcomes from S, P(A)=


Example
Example

  • A coin is tossed 1 time

  • S=

  • Probability of observing a heads or tails is


Example1
Example

  • A coin is tossed 2 times

  • S=

  • What is the probability of getting either two heads or two tails?

  • What is the probability of getting either one heads or two heads?


Example2
Example

  • Inherited characteristics are transmitted from one generation to the next by genes

  • Genes occur in pairs and offspring receive one from each parent

  • Experiment was conducted to verify this idea

  • Pure red flower crossed with a pure white flower gives

  • Two of these hybrids are crossed. Outcomes:

  • Probability of each outcome


Note

  • Sometimes, not all outcomes are equally likely (e.g., fixed die)

  • Recall, probability of an event is long-term proportion of times the event occurs when the experiment is performed repeatedly

  • NOTE: Probability refers to experiments or processes, not individuals


Probability rules
Probability Rules

  • Have looked at computing probability for events

  • How to compute probability for multiple events?

  • Example: 65% of SFU Business School Professors read the Wall Street Journal, 55% read the Vancouver Sun and 45% read both. A randomly selected Professor is asked what newspaper they read. What is the probability the Professor reads one of the 2 papers?



  • Example: 65% of SFU Business School Professors read the Wall Street Journal, 55% read the Vancouver Sun and 45% read both. A randomly selected Professor is asked what newspaper they read. What is the probability the Professor reads one of the 2 papers?


Counting and combinatorics
Counting and Combinatorics

  • In the equally likely case, computing probabilities involves counting the number of outcomes in an event

  • This can be hard…really

  • Combinatorics is a branch of mathematics which develops efficient counting methods

  • These methods are often useful for computing probabilites


Combinatorics
Combinatorics

  • Consider the rhyme

    As I was going to St. Ives

    I met a man with seven wives

    Every wife had seven sacks

    Every sack had seven cats

    Every cat had seven kits

    Kits, cats, sacks and wives

    How many were going to St. Ives?

  • Answer:


Example3
Example

  • In three tosses of a coin, how many outcomes are there?


Product rule
Product Rule

  • Let an experiment E be comprised of smaller experiments E1,E2,…,Ek, where Ei has ni outcomes

  • The number of outcome sequences in E is (n1n2n3 …nk )

  • Example (St. Ives re-visited)


Example4
Example

  • In a certain state, automobile license plates list three letters (A-Z) followed by four digits (0-9)

  • How many possible license plates are there?


Tree diagram
Tree Diagram

  • Can help visualize the possible outcomes

  • Constructed by listing the posbilites for E1 and connecting these separately to each possiblility for E2, and so on


Example5
Example

  • In three tosses of a coin, how many outcomes are there?


Example permuatation
Example - Permuatation

  • Suppose have a standard deck of 52 playing cards (4 suits, with 13 cards per suit)

  • Suppose you are going to draw 5 cards, one at a time, with replacement (with replacement means you look at the card and put it back in the deck)

  • How many sequences can we observe


Permutations
Permutations

  • In previous examples, the sample space for Ei does not depend on the outcome from the previous step or sub-experiment

  • The multiplication principle applies only if the number of outcomes for Ei is the same for each outcome of Ei-1

  • That is, the outcomes might change change depending on the previous step, but the number of outcomes remains the same


Permutations1
Permutations

  • When selecting object, one at a time, from a group of N objects, the number of possible sequences is:

  • The is called the number of permutations of n things taken k at a time

  • Sometimes denoted Pk,n

  • Can be viewed as number of ways to select k things from n objects where the order matters


Permutations2
Permutations

  • The number of ordered sequences of k objects taken from a set of n distinct objects (I.e., number of permutations) is:

  • Pk,n=n(n-1) … (n-k+1)


Example6
Example

  • Suppose have a standard deck of 52 playing cards (4 suits, with 13 cards per suit)

  • Suppose you are going to draw 5 cards, one at a time, without replacement

  • How many permutations can we observe


Combinations
Combinations

  • If one is not concerned with the order in which things occur, then a set of k objects from a set with n objects is called a combination

    Example

    • Suppose have 6 people, 3 of whom are to be selected at random for a committee

    • The order in which they are selected is not important

    • How many distinct committees are there?


Combinations1
Combinations

  • The number of distinct combinations of k objects selected from n objects is:

  • “n choose k”

  • Note: n!=n(n-1)(n-2)…1

  • Note: 0!=1

  • Can be viewed as number of ways to select mthings taken k at a time where the order does not matter


Combinations2
Combinations

Example

  • Suppose have 6 people, 3 of whom are to be selected at random for a committee

  • The order in which they are selected is not important

  • How many distinct committees are there?


Example7
Example

  • A committee of size three is to be selected from a group of 4 Conservatives, 3 Liberals and 2 NDPs

  • How many committees have a member from each group?

  • What is the probability that there is a member from each group on the committee?


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