1 / 22

5-Minute Check on Section 6-1

5-Minute Check on Section 6-1. If two events do not affect each other, then they are called? What type of probabilities are probability models used for?

turbeville
Download Presentation

5-Minute Check on Section 6-1

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 5-Minute Check on Section 6-1 If two events do not affect each other, then they are called? What type of probabilities are probability models used for? Starting with the first row of random digits in your book, simulate getting a snow day for 25 days; given that the probability of it snowing is 10% and the probability of a snow day given that it snowed is 50%. independent theoretical Assignment: 0: snow; 1-9: no snow; 0-4: snow day; 5-9: school 19223 95034 05756 28713 96409 12531 42544 82853 ↑ ↓ ↑↓ ↑↓ On the 8th day it snowed and we missed school; on the 10th day it snowed and we were in school and on the 22nd day it snowed and we were in school. Click the mouse button or press the Space Bar to display the answers.

  2. Lesson 6 – 2a Probability Models

  3. Knowledge Objectives • Explain what is meant by random phenomenon. • Explain what it means to say that the idea of probability is empirical. • Define probability in terms of relative frequency. • Define sample space. • Define event.

  4. Knowledge Objectives Cont • Explain what is meant by a probability model. • List the four rules that must be true for any assignment of probabilities. • Explain what is meant by equally likely outcomes. • Define what it means for two events to be independent. • Give the multiplication rule for independent events.

  5. Construction Objectives • Explain how the behavior of a chance event differs in the short- and long-run. • Construct a tree diagram. • Use the multiplication principle to determine the number of outcomes in a sample space. • Explain what is meant by sampling with replacement and sampling without replacement. • Explain what is meant by {AB} and {AB}. • Explain what is meant by each of the regions in a Venn diagram.

  6. Construction Objectives Cont • Give an example of two events A and B where AB = . • Use a Venn diagram to illustrate the intersection of two events A and B. • Compute the probability of an event given the probabilities of the outcomes that make up the event. • Compute the probability of an event in the special case of equally likely outcomes. • Given two events, determine if they are independent.

  7. Vocabulary • Empirical – based on observations rather than theorizing • Random – individuals outcomes are uncertain • Probability – long-term relative frequency • Tree Diagram – allows proper enumeration of all outcomes in a sample space • Sampling with replacement – samples from a solution set and puts the selected item back in before the next draw • Sampling without replacement – samples from a solution set and does not put the selected item back

  8. Vocabulary Cont • Union – the set of all outcomes in both subsets combined (symbol: ) • Empty event – an event with no outcomes in it (symbol: ) • Intersect – the set of all in only both subsets (symbol: ) • Venn diagram – a rectangle with solution sets displayed within • Independent – knowing that one thing event has occurred does not change the probability that the other occurs • Disjoint – events that are mutually exclusive (both cannot occur at the same time)

  9. Idea of Probability Chance behavior is unpredictable in the short run, but has a regular and predictable pattern in the long run The unpredictability of the short run entices people to gamble and the regular and predictable pattern in the long run makes casinos very profitable.

  10. Randomness and Probability We call a phenomenon random if individual outcomes are uncertain but there is nonetheless a regular distribution of outcomes in a large number of repetitions The probability of any outcome of a random phenomenon is the proportion of times the outcome would occur in a very long series of repetitions. That is, probability is long-term frequency.

  11. Example 1 Using the PROBSIM application on your calculator flip a coin 1 time and record the results? Now flip it 50 times and record the results. Now flip it 200 times and record the results. (Use the right and left arrow keys to get frequency counts from the graph) • 0 1 • 33 • 117 134

  12. Probability Models Probability model is a mathematical description of a random phenomenon consisting of two parts: a sample space S and a way of assigning probabilities to events S E F 5 2 1 4 3 6 Sample Space S: possible outcomes in rolling a six-sided die Event E: odd numbered outcomes Event F: even numbered outcomes

  13. Example 2 Draw a Venn diagram to illustrate the following probability problem: what is the probability of getting a 5 on two consecutive rolls of the dice? S E F 1 4 1 2 5 2 4 6 3 6 3

  14. Tree Diagrams Tree Diagram makes the enumeration of possible outcomes easier to see and determine N HTT HTH HHT HHH N Y Y N Y Y Event 1 Event 2 Event 3 Outcomes N TTT TTH THT THH N Y N N Y Y Running the tree out details an individual outcome

  15. Example 3 Given a survey with 4 “yes or no” type questions, list all possible outcomes using a tree diagram. Divide them into events (number of yes answers) regardless of order.

  16. Example 3 cont YNNN YNNY YNYN YNYY YYNN YYNY YYYN YYYY N N Y N N Y Y Y N N Y Y N Y Y Q 1 Q 2 Q 3 Outcomes Q 4 NNNN NNNY NNYN NNYY NYNN NYNY NYYN NYYY N N Y N N Y Y N N N Y Y N Y Y

  17. Example 3 cont YNNN 1 YNNY 2 YNYN 2 YNYY 3 YYNN 2 YYNY 3 YYYN 3 YYYY 4 NNNN 0 NNNY 1 NNYN 1 NNYY 2 NYNN 1 NYNY 2 NYYN 2 NYYY 3 Outcomes

  18. Multiplication Rule If you can do one task in n number of ways and a second task in m number of ways, then both tasks can be done in nm number of ways.

  19. Example 4 How many different dinner combinations can we have if you have a choice of 3 appetizers, 2 salads, 4 entrees, and 5 deserts? 3  2  4  5 = 120 different combinations

  20. Replacement • With replacement maintains the original probability • Draw a card and replace it and then draw another • What are your odds of drawing two hearts? • Without replacement changes the original probability • Draw two cards • What are you odds of drawing two hearts • How have the odds changed? • Events are now dependent

  21. Example 5 From our previous slide: • With Replacement: (13/52) (13/52) = 1/16 = 0.0625 • Without Replacement (13/52) (12/51) = 0.0588

  22. Summary and Homework • Summary • Probability is the proportion of times an event occurs in many repeated trials • Probability model consist of the entire space of outcomes and associated probabilities • Sample space is the set of all possible outcomes • Events are subsets of outcomes in the sample space • Tree diagram helps show all possible outcomes • Multiplication principle enumerates possible outcomes • Sample with replacement keeps original probability • Sample without replacement changes original probability • Homework • Day One: pg 397 6-22, 24, 25, 29, 34, 36

More Related