1. Sta220 - Statistics
Mr. Smith
Room 310
Class #7

2. Questions about the project?

3. Where We’ve Been
Identified the objective of inferential statistics: to make inferences about a population on the basis of information in a sample
Introduced graphical and numerical descriptive measure for both quantitative and qualitative data.

4. Where We’re Going Develop probability as a measure of uncertainty
Introduce basic rules for finding probabilities
Use a probability as a measure of reliability for an inference
Use a probability in random sampling

5. Section 3.1

6. An experiment is an act or process of observation that leads to a single outcome that cannot be predicted with certainty. A sample point is the most basic outcome of an experiment.

9. Example: Listing Sample Point for a Coin-Tossing Experiment
Two coins are tossed, and their up faces are recorded. List all sample points for this experiment.

10. Figure 3.1 Tree diagram for the coin-tossing experiment
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11. Figure 3.2 Venn diagrams for the three experiments from Table 3.1
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12. The sample space of an experiment is the collection of all its sample points.

13. Table 3.1
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14. The probability of sample point is a number between 0 and 1 which measures the likelihood that the outcome will occur when the experiment is performed.

15. Probability Notation
Probabilities can be expressed as a fraction (always reduced), decimal, or percent.
P(A) = means the probability of event A is 0.123
Unless noted, round probabilities to three decimal places.

16. Law of Large Numbers states the relative frequency of the number of times that an outcome occurs when an experiment is replicated over and over again approaches the true probability of the outcome.

17. Figure 3.3 Proportion of heads in N tosses of a coin
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18. Procedure
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19. All probabilities are between 0 and 1, inclusive.
EXAMPLE A Probability Model
In a bag of peanut M&M milk chocolate candies, the colors of the candies can be brown, yellow, red, blue, orange, or green. Suppose that a candy is randomly selected from a bag. The table shows each color and the probability of drawing that color. Verify this is a probability model.
Color
Probability
Brown
0.12
Yellow
0.15
Red
Blue
0.23
Orange
Green
All probabilities are between 0 and 1, inclusive.
Because = 1, rule 2 (sum of all probabilities must equal 1) is satisfied.

20. Example: Die Tossing Experiment
A fair die is tossed, and the up face is observed. If the face is even, you win $1. Otherwise, you lose $1. What is the probability that you win?

21. Example: Die Tossing Experiment
The sample space is S: {1, 2, 3, 4, 5, 6} Since the die is balanced, we assign a probability of 1/6 to each sample points in the sample space. 𝑃 𝐴 = = 1 2

22. Figure 3.5 Die-toss experiment with event A, observe an even number
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23. An event is a specific collection of sample points.

24. Example: Probability of an Event-Coin-Tossing Experiment
Consider the experiment of tossing two unbalanced coins. Because the coins are not balanced, their outcomes ( H or T) are not equiprobable. Suppose the correct probabilities associated with the sample points are given in the accompanying table. [Note: The necessary for assigning the probabilities to sample points are satisfied.] Consider the event

25. A: {Observe exactly one head} B: {Observe at least one head} Calculate the probability of A and the probability of B.
Sample Point
Probability HH
4/9 HT
2/9 TH TT
1/9

26. 𝑃 𝐴 = 𝑃 𝐻𝑇 + 𝑃 𝑇𝐻 = = 4 9 So 𝑃 𝐴 = 4 9 𝑃 𝐵 = 𝑃 𝐻𝐻 +𝑃 𝐻𝑇 +𝑃 𝑇𝐻 = = 8 9 So 𝑃(𝐵) = 8/9

27. Probability of an Event
The probability of an event A is calculated by summing the probabilities of the sample points in the sample space for A.

28. Steps for Calculating Probabilities of Events
Define the experiment: that is, describe the process used to make an observation and the type of observation that will be recorded.
List of the sample points.
Assign probabilities to the sample points.
Determine the collection of sample points contained in the even of interest.
Sum the sample point probabilities to get the probability of the event.

29. Combinations Rule

30. Example: Selecting 5 Movies from
Suppose a movie reviewer for a newspaper reviews 5 movies each month. This month, the reviewer has 20 new movies from which to make the selection. How many different samples of 5 movies can be selected from the 20?

31. N = 20 and n = = 20! 5! 20−5 ! = 20! 5!15! = 20∙19∙18∙…∙3∙2∙1 5∙4∙3∙2∙1 15∙14∙13…3∙2∙1 = 20∙19∙18∙17∙16 5∙4∙3∙2∙1 =15,504