1. Lecture Slides Elementary Statistics Twelfth Edition
and the Triola Statistics Series
by Mario F. Triola

2. 4-2 Definitions
Event
any collection of results or outcomes of a procedure
Simple Event
an outcome or an event that cannot be further broken down into simpler components
Sample Space
for a procedure consists of all possible simple events; that is, the sample space consists of all outcomes that cannot be broken down any further

3. Groupwork Examples Ex1.) List the sample spaces of the following.
a.) Flip a coin.
S ={ Heads, Tails}
b.) Flip a coin two times.
S = { HH, HT, TH, TT }
c.) The gender of two babies.
d.) Two dice are rolled.

4. Notation for Probabilities
P - denotes a probability.
A, B, and C - denote specific events.
P(A) - denotes the probability of event A occurring.

5. Computing Probability
Basic Rules for
Computing Probability
Classical Approach to Probability (Requires Equally Likely Outcomes)
Assume that a given procedure has n different simple events and that each of those simple events has an equal chance of occurring. If event A can occur in s of these n ways, then

6. Groupwork Examples Ex2.) A fair dice is rolled.
a.) Find the Sample Space.
S = {1, 2, 3, 4, 5, 6}
b.) Fill in the following events:
c.) Find P(A), P(B), P(C), and P(D).
Name
Description
Events
A =
an odd number occurred =
= { }
B =
an even number occurrded=
C =
a number less than 5 occurred=
D =
a number that does not start
with the letter 't' or 'f'

7. Probability Limits
The probability of an impossible event is 0.
Always express a probability as a fraction or decimal number between 0 and 1.
The probability of an event that is certain to occur is 1.
For any event A, the probability of A is between 0 and 1 inclusive.
That is,

8. Possible Values for Probabilities

9. Definition
An event is unlikely if its probability is very small, such as 0.05 or less.
An event has an usually low number of outcomes of a particular type or an unusually high number of those outcomes if that number is far from what we typically expect.

10. 4-3 Compound Event Formal Addition Rule
P(A or B) = P(A) + P(B) – P(A and B)
where P(A and B) denotes the probability that A and B both occur at the same time as an outcome in a trial of a procedure.

11. Disjoint or Mutually Exclusive
Events A and B are disjoint (or mutually exclusive) if they cannot occur at the same time. (That is, disjoint events do not overlap.)
Venn Diagram for Events That Are Not Disjoint
Venn Diagram for Disjoint Events

12. Complementary Events A and must be disjoint.
It is impossible for an event and its complement to occur at the same time.

13. Venn Diagram for the Complement of Event A

14. 4-5Complements: The Probability of “At Least One”
“At least one” is equivalent to “one or more.”
The complement of getting at least one item of a particular type is that you get no items of that type.

15. 4-6Key Concept
In many probability problems, the big obstacle is finding the total number of outcomes, and this section presents several methods for finding such numbers without directly listing and counting the possibilities.

16. Fundamental Counting Rule
For a sequence of two events in which the first event can occur m ways and the second event can occur n ways, the events together can occur a total of mn ways.

17. (when items are all different)
Permutations Rule
(when items are all different)
Requirements:
There are n different items available. (This rule does not apply if some of the items are identical to others.)
We select r of the n items (without replacement).
We consider rearrangements of the same items to be different sequences. (The permutation of ABC is different from CBA and is counted separately.)
If the preceding requirements are satisfied, the number of permutations (or sequences) of r items selected from n available items (without replacement) is

18. Combinations Rule Requirements: There are n different items available.
We select r of the n items (without replacement).
We consider rearrangements of the same items to be the same. (The combination of ABC is the same as CBA.)
If the preceding requirements are satisfied, the number of combinations of r items selected from n different items is

19. Permutations versus Combinations
When different orderings of the same items are to be counted separately, we have a permutation problem, but when different orderings are not to be counted separately, we have a combination problem.

20. 5-1 Review and Preview
This chapter combines the methods of descriptive statistics presented in Chapter 2 and 3 and those of probability presented in Chapter 4 to describe and analyze probability distributions.
Probability distributions describe what will probably happen instead of what actually did happen, and they are often given in the format of a graph, table, or formula.
Emphasize the combination of the methods in Chapter 3 (descriptive statistics) with the methods in Chapter 4 (probability).
Chapter 3 one would conduct an actual experiment and find and observe the mean, standard deviation, variance, etc. and construct a frequency table or histogram
Chapter 4 finds the probability of each possible outcome
Chapter 5 presents the possible outcomes along with the relative frequencies we expect
Page 200 of Elementary Statistics, 10th Edition

21. 5-2 Random Variable Probability Distribution
Random Variable a variable (typically represented by x) that has a single numerical value, determined by chance, for each outcome of a procedure
Probability Distribution a description that gives the probability for each value of the random variable, often expressed in the format of a graph, table, or formula

22. Probability Distribution: Requirements
There is a numerical random variable x and its values are associated with corresponding probabilities.
The sum of all probabilities must be 1.
Each probability value must be between 0 and 1 inclusive.
This chapter deals exclusively with discrete random variables - experiments where the data observed is a ‘countable’ value. Give examples.
Following chapters will deal with continuous random variables.
Page 201 of Elementary Statistics, 10th Edition

23. Standard Deviation of a Probability Distribution
Mean, Variance and
Standard Deviation of a
Probability Distribution
Mean
Variance
Variance (shortcut)
Standard Deviation
In Chapter 3, we found the mean, standard deviation,variance, and shape of the distribution for actual observed experiments.
The probability distribution and histogram can provide the same type information.
These formulas will apply to ANY type of probability distribution as long as you have have all the P(x) values for the random variables in the distribution.
In section 4 of this chapter, there will be special EASIER formulas for the special binomial distribution.
The TI-83 and TI-83 Plus calculators can find the mean, standard deviation, and variance in the same way that one finds those values for a frequency table.
With the TI-82, TI-81, and TI-85 calculators, one would have to multiply all decimal values in the P(x) column by the same factor so that there were no decimals and proceed as usual.
Page 204 of Elementary Statistics, 10th Edition

24. Example
The following table describes the probability distribution for the number of girls in three births.
Find the mean, variance, and standard deviation. x
P(x)
Also called expectation or mathematical expectation
Plays a very important role in decision theory
page 208 of Elementary Statistics, 10th Edition

25. Identifying Unusual Results
Range Rule of Thumb
According to the range rule of thumb, most values should lie within 2 standard deviations of the mean.
We can therefore identify “unusual” values by determining if they lie outside these limits:
Maximum usual value =
Minimum usual value =
Page of Elementary Statistics, 10th Edition

26. Example – continued
We found for families with two children, the mean number of girls is 1.5 and the standard deviation is .87 girls.
Use those values to find the maximum and minimum usual values for the number of girls.
Page of Elementary Statistics, 10th Edition

27. Identifying Unusual Results
Probabilities
Using Probabilities to Determine When Results Are Unusual
Unusually high: x successes among n trials is an unusually high number of successes if
Unusually low: x successes among n trials is an unusually low number of successes if
The discussion of this topic in the text includes some difficult concepts, but it also includes an extremely important approach used often in statistics.
Page of Elementary Statistics, 10th Edition

28. 5-3 Key Concept
This section presents a basic definition of a binomial distribution along with notation and methods for finding probability values.
Binomial probability distributions allow us to deal with circumstances in which the outcomes belong to two relevant categories such as acceptable/defective or survived/died.

29. Binomial Probability Distribution
A binomial probability distribution results from a procedure that meets all the following requirements:
1. The procedure has a fixed number of trials.
2. The trials must be independent. (The outcome of any individual trial doesn’t affect the probabilities in the other trials.)
3. Each trial must have all outcomes classified into two categories (commonly referred to as success and failure).
Binomial probability distributions are important because they allow us to deal with circumstances in which the outcomes belong to TWO categories, such as pass/fall, acceptable/defective, etc.
page 214 of Elementary Statistics, 10th Edition
4. The probability of a success remains the same in all trials.

30. Notation for Binomial Probability Distributions
S and F (success and failure) denote the two possible categories of all outcomes; p and q will denote the probabilities of S and F, respectively, so
(p = probability of success)
(q = probability of failure)
The word success is arbitrary and does not necessarily represent something GOOD. If you are trying to find the probability of deaths from hang-gliding, the ‘success’ probability is that of dying from hang-gliding.
Remind students to carefully look at the probability (percentage or rate) provided and whether it matches the probability desired in the question. It might be necessary to determine the complement of the given probability to establish the ‘p’ value of the problem.
Page 214 of Elementary Statistics, 10th Edition.

31. Notation (continued) n denotes the fixed number of trials.
x denotes a specific number of successes in n trials, so x can be any whole number between 0 and n, inclusive.
p denotes the probability of success in one of the n trials.
q denotes the probability of failure in one of the n trials.
Page 214 of Elementary Statistics, 10th Edition.
P(x) denotes the probability of getting exactly x successes among the n trials.

32. Method 1: Using the Binomial
Probability Formula
where
n = number of trials
x = number of successes among n trials
p = probability of success in any one trial
q = probability of failure in any one trial (q = 1 – p)
page 216 of Elementary Statistics, 10th Edition

33. Example
Given there is a 0.85 probability that any given adult knows of Twitter, use the binomial probability formula to find the probability of getting exactly three adults who know of Twitter when five adults are randomly selected.
We have:
We want:
Page 216 of Elementary Statistics, 10th Edition. =

34. 5-4 Binomial Distribution: Formulas
Mean
Variance
Std. Dev.
Where
n = number of fixed trials
p = probability of success in one of the n trials
q = probability of failure in one of the n trials
The obvious advantage to the formulas for the mean, standard deviation, and variance of a binomial distribution is that you do not need the values in the distribution table. You need only the n, p, and q values.
A common error students tend to make is to forget to take the square root of (n)(p)(q) to find the standard deviation, especially if they used L1 and L2 lists in a graphical calculator to find the standard deviation with non-binomial distributions. The square root is built into the programming of the calculator and students do not have to remember it. These formulas do require the student to remember to take the square root of the three factors.
Page 225 of Elementary Statistics, 10th Edition

35. Example
McDonald’s has a 95% recognition rate. A special focus group consists of 12 randomly selected adults.
For such a group, find the mean and standard deviation.
Page 225 of Elementary Statistics, 10th Edition.