LECTURE 6

Randomness and Probability

RANDOM PHENOMENA AND PROBABILITY

• With random phenomena, we can’t predict the individual outcomes, but we can hope to understand characteristics of their long-run behavior.

• For any random phenomenon, each attempt, or trial, generates an outcome.

• We use the more general term event to refer to outcomes or combinations of outcomes.

SAMPLE SPACES

• A sample space is a special event that is the collection of all possible outcomes.

• We denote the sample space S or sometimes Ω (omega)

• The probability of an event is its long-run relative frequency.

• Independence means that the outcome of one trial doesn’t influence or change the outcome of another.

LAW OF LARGE NUMBERS

•The Law of Large Numbers (LLN) states that if the events are independent, then as the number of trials increases, the long-run relative frequency of an event gets closer and closer to a single (true) value.

•Empirical probability is based on repeatedly observing the event’s outcome.

LAW OF AVERAGES

• Many people confuse the Law of Large Numbers with the so-called Law of Averages

• The Law of Averages doesn’t exist.

• Presumably, the Law of Averages would say that things have to even out in the short run.

• No luck this time, more luck next time.

• The (theoretical) probability of event A occurring can be computed with the following equation:

TYPES OF PROBABILITY:THEORETICAL PROBABILITY

TYPES OF PROBABILITY:PERSONAL PROBABILITY

• A subjective, or personal probability expresses your uncertainty about the outcome.

• Although personal probabilities may be based on experience, they are not based either on long-run relative frequencies or on equally likely events.

• If the probability of an event occurring is 0, the event can’t occur.

• If the probability is 1, the event always occurs.

• For any event A, also written as

PROBABILITY RULES:RULE 1

• The Probability Assignment Rule

• The probability of the set of all possible outcomes must be 1.

• where S represents the set of all possible outcomes and is called the sample space.

PROBABILITY RULES:RULE 2

• The Complement Rule

• The probability of an event occurring is 1 minus the probability that it doesn’t occur.

• where the set of outcomes that are not in event is called the “complement” of , and is denoted .

PROBABILITY RULES:RULE 3

PROBABILITY RULES:EXAMPLE

• Lee’s Lights sell lighting fixtures. Lee records the behavior of 1000 customers entering the store during one week. Of those, 300 make purchases. What is the probability that a customer doesn’t make a purchase?

• If P(Purchase) = 0.30, then

• P(no purchase) = 1 – P(Purchase) =1 – 0.30 = 0.70

• The Multiplication Rule

• For two independent events A and B, the probability that both, A and B, occur is the product of the probabilities of the two events.

• provided that A and B are independent.

PROBABILITY RULES:RULE 4

• Whether or not a caller qualifies for a platinum credit card is a random outcome. Suppose the probability of qualifying is 0.35. What is the chance that the next two callers qualify?

• Since the two different callers are independent, then

P(customer 1 qualifies and customer 2 qualifies)

= P(customer 1 qualifies) x P(customer 2 qualifies)

= 0.35 x 0.35

= 0.1225

PROBABILITY RULES:EXAMPLE

• The Addition Rule

• Two events are disjoint (or mutually exclusive) if they have no outcomes in common.

• The Addition Rule allows us to add the probabilities of disjoint events to get the probability that either event occurs.

• where and are disjoint.

PROBABILITY RULES:RULE 5

PROBABILITY RULES:EXAMPLE

• Some customers prefer to see the merchandise but then make their purchase online. Lee determines that there’s an 8% chance of a customer making a purchase in this way. We know that about 30% of customers make purchases when they enter the store. What is the probability that a customer who enters the store makes no purchase at all?

P(purchase in the store or online)

= P (purchase in store) + P(purchase online)

= 0.30 + 0.08

= 0.38

P(no purchase) = 1 – 0.38 = 0.62

• The General Addition Rule

• The General Addition Rule calculates the probability that either of two events occurs. It does not require that the events be disjoint.

• Recall

PROBABILITY RULES:RULE 6

PROBABILITY RULES:EXAMPLE

• Lee notices that when two customers enter the store together, their behavior isn’t independent. In fact, there’s a 20% chance they’ll both make a purchase. When two customers enter the store together, what is the probability that at least one of them will make a purchase?

PROBABILITY RULES:EXAMPLE

• Car Inspections

• You and a friend get your cars inspected. The event of your car’s passing inspection is independent of your friend’s car. If 75% of cars pass inspection what is the probability that

• Your car passes inspection?

• Your car doesn’t pass inspection?

• Both cars pass inspection?

• At least one of two cars passes?

• Neither car passes inspection?

PROBABILITY RULES: EXAMPLE• You and a friend get your cars inspected. The event of your

car’s passing inspection is independent of your friend’s car. If 75% of cars pass inspection what is the probability that

• Your car passes inspection?

• Your car doesn’t pass inspection?

• Both cars pass inspection?

• At least one of two cars passes?

1 – (0.25)2 = 0.9375 OR0.75 + 0.75 – 0.5625 = 0.9375

• Neither car passes inspection?

1 – 0.9375 = 0.0625

CONTINGENCY TABLES

• Events may be placed in a contingency table such as the one in the example below.

• As part of a Pick Your Prize Promotion, a store invited customers to choose which of three prizes they’d like to win. The responses could be placed in the following contingency table:

MARGINAL PROBABILITY

• Marginal probability depends only on totals found in the margins of the table.

• In the table below, the probability that a respondent chosen at random is a woman has a marginal probability of

MARGINAL PROBABILITY

JOINT PROBABILITIES

• Joint probabilities give the probability of two events occurring together.

• Each row or column shows a conditional distribution given one event.

• In the table above, the probability that a selected customer wants a bike, given that we have selected a woman is:

CONDITIONAL DISTRIBUTION

• In general, when we want the probability of an event from a conditional distribution, we write P(B|A) and pronounce it “the probability of B given A.”

• A probability that takes into account a given condition is called a conditional probability.

CONDITIONAL PROBABILITY

• The General Multiplication Rule

• The General Multiplication Rule calculates the probability that both of two events occurs. It does not require that the events be independent.

PROBABILITY RULES:RULE 7

• What is the probability that a randomly selected customer wants a bike if the customer selected is a woman?

PROBABILITY RULES:EXAMPLE

PROBABILITY RULES:EXAMPLE

• Are Prize preference and Sex independent? If so,P(bike|woman) will be the same as P(bike). Are they equal?

• P(bike|woman)= 30/251 = 0.12

• P(bike) = 90/478 = 0.265

• 0.12 ≠ 0.265

• Since the two probabilites are not equal, Prize preference and Sex and not independent.

INDEPENDENCE

• Events A and B are independent whenever

• Independent vs. Disjoint

• For all practical purposes, disjoint events cannot be independent.

• Don’t make the mistake of treating disjoint events as if they were independent and applying the Multiplication Rule for independent events.

CONSTRUCTING CONTINGENCY TABLES

• If you’re given probabilities without a contingency table, you can often construct a simple table to correspond to the probabilities and use this table to find other probabilities.

CONSTRUCTING CONTINGENCY TABLES

• A survey classified homes into two price categories (Low and High). It also noted whether the houses had at least 2 bathrooms or not (True or False). 56% of the houses had at least 2 bathrooms, 62% of the houses were Low priced, and 22% of the houses were both. Translating the percentages to probabilities, we have:

CONSTRUCTING CONTINGENCY TABLES

• The 0.56 and 0.62 are marginal probabilities, so they go in the margins.

• The 22% of houses that were both Low priced and had at least 2 bathrooms is a joint probability, so it belongs in the interior of the table.

CONSTRUCTING CONTINGENCY TABLES

• The 0.56 and 0.62 are marginal probabilities, so they go in the margins.

• The 22% of houses that were both Low priced and had at least 2 bathrooms is a joint probability, so it belongs in the interior of the table.

• Because the cells of the table show disjoint events, the probabilities always add to the marginal totals going across rows or down columns.

Constructing Contingency Tables

CONSTRUCTING CONTINGENCY TABLES: EXAMPLE

• A national survey indicated that 30% of adults conduct their banking online. It also found that 40% under the age of 50, and that 25% under the age of 50 and conduct their banking online.

• What percentage of adults do not conduct their banking online?

• What type of probability is the 25% mentioned above?

• Construct a contingency table showing joint and marginal probabilities.

• What is the probability that an individual who is under the age of 50 conducts banking online?

• Are Banking online and Age independent?

Constructing Contingency Tables

CONSTRUCTING CONTINGENCY TABLES: EXAMPLE

• What percentage of adults do not conduct their banking online?

100% – 30% = 70%

• What type of probability is the 25% mentioned above?

Marginal

• Construct a contingency table showing joint and marginal probabilities.

CONSTRUCTING CONTINGENCY TABLES: EXAMPLE

• What is the probability that an individual who is under the age of 50 conducts banking online?

0.25/0.40 = 0.625

• Are Banking online and Age independent?

No. P(banking online|under 50) = 0.625, which is not equal to P(banking online) = 0.30.

QUICK SUMMARY

• Beware of probabilities that don’t add up to 1.

• Don’t add probabilities of events if they’re not disjoint.

• Don’t multiply probabilities of events if they’re not independent.

• Don’t confuse disjoint and independent.

LEARNING OUTCOMES

• Apply the facts about probability to determine whether an assignment of probabilities is legitimate.

• Probability is long-run relative frequency.

• Individual probabilities must be between 0 and 1.

• The sum of probabilities assigned to all outcomes must be 1.

• Understand the Law of Large Numbers and that the common understanding of the “Law of Averages” is false.

LEARNING OUTCOMES

• Know the 7 rules of probability and how to apply them.

• Know how to construct and read a contingency table.

• Know how to define and use independence. .