1. Chapter 12 and 8-5 Notes

2. 12-1 Frequency Tables, Line Plots, and Histograms Frequency Table: lists each data item with the number of times it occurs. Line Plot: displays data with X marks above a number line. Histogram: shows the frequencies of data items as a graph.

3. 12-1 Frequency Tables, Line Plots, and Histograms Range : _______

4. 12-1 Frequency Tables, Line Plots, and Histograms-answers Range : _4______ 3 4 5 6 7 5 3 1 1 2

5. 12-1 Frequency Tables, Line Plots, and Histograms Range : _______

6. 12-1 Frequency Tables, Line Plots, and Histograms-answers Range : 4 5 2 0 4 1 0 1 2 3 4

7. 12-1 Frequency Tables, Line Plots, and Histograms

8. 12-1 Frequency Tables, Line Plots, and Histograms-answers

9. 12-3 Using Graphs to Persuade You can draw graphs of data in different ways in order to give different impressions. You can use a break in the scale on one or both axes of a line graph or a bar graph. This lets you show more detail and emphasize differences. It can also give you a distorted view of the data.

10. 12-3 Using Graphs to Persuade

11. 12-3 Using Graphs to Persuade-answers 1. American Ampersand 2. Fossil Week 3. You might compare lengths of the bars without noticing the break in the scale.

12. 12-3 Using Graphs to Persuade

13. 12-3 Using Graphs to Persuade-answers

14. 12-2 Box-and-Whisker Plots A box-and-whisker plot: displays the distribution of data items along a number line. Quartiles: divide the data into four equal parts. The median is the middle quartile.

15. 12-2 Box-and-Whisker Plots

16. 12-2 Box-and-Whisker Plots- answers 98 80.5 118

17. 12-2 Box-and-Whisker Plots

18. 12-2 Box-and-Whisker Plots- answers 13 4 21

19. 8-5 Scatter Plots Scatter Plot: a graph that shows the relationship between two sets of data. Graph data as ordered pairs to make scatter plots.

20. 8-5 Scatter Plots

22. 8-5 Scatter Plots-answers Positive correlation Negative correlation No correlation

23. 12-4 Counting Outcomes and Theoretical Probability To count possible outcomes you can use a tree diagram.

24. 12-4 Counting Outcomes and Theoretical Probability-answers To count possible outcomes you can use a tree diagram. 6 choices AM, AN, BM, BN, CM, CN 8 choices, P1C1, P1C2, P2C1, P2C2, P3C1, P3C2, P4C1, P4C2

25. 12-4 Counting Outcomes and Theoretical Probability To count possible outcomes you can use a tree diagram or count choices using the Counting Principle. Counting Principle: If there are m ways of making one choice, and n ways of making a second choice, then there are m * n ways of making the first choice followed by the second.

26. 12-4 Counting Outcomes and Theoretical Probability Use the Counting Principle to solve each problem.

27. 12-4 Counting Outcomes and Theoretical Probability- answers Use the Counting Principle to solve each problem. 5 * 7 * 4 = 140 ways 4 * 13 * 9 = 468 combinations

28. 12-4 Counting Outcomes and Theoretical Probability Theoretical Probability: P(event) = number of favorable outcomes number of possible outcomes

29. 12-4 Counting Outcomes and Theoretical Probability-answers Theoretical Probability: P(event) = number of favorable outcomes number of possible outcomes m1A, m1B, m1C, m2A, m2B, m2C, m3A, m3B, m3C, m4A, m4B, m4C 3/12 simplified to 1/3 1/12

30. Use a tree diagram to find the sample space for tossing two coins. Then find the probability.  P(two heads)  P(one tail, one head) Use counting principle to help you find each probability.  Choosing three winning lottery numbers when the numbers are chosen at random from 1 to 30. Numbers can repeat.

31. Answers Use a tree diagram to find the sample space for tossing two coins. Then find the probability.  P(two heads) – 1/4  P(one tail, one head) – 1/2 Use counting principle to help you find each probability.  Choosing three winning lottery numbers when the numbers are chosen at random from 1 to 30. Numbers can repeat. 1/90

32. 12-5 Independent and Dependent Events Independent events: events for which the occurrence of one event does not affect the probability of the occurrence of the other. Probability of Independent Events: P(A, then B) = P(A) * P(B)

33. 12-5 Independent and Dependent Events Probability of Independent Events: P(A, then B) = P(A) * P(B)

34. 12-5 Independent and Dependent Events-answers Probability of Independent Events: P(A, then B) = P(A) * P(B) 1/36 2/36 or 1/18 1/36 3/36 or 1/12 6/36 or 1/6 9/36 or 1/4

35. 12-5 Independent and Dependent Events Dependent events: events for which the occurrence of one event affects the probability of the occurrence of the other. Probability of Dependent Events: P(A, then B) = P(A) * P(B after A)

36. 12-5 Independent and Dependent Events Probability of Dependent Events: P(A, then B) = P(A) * P(B after A)

37. 12-5 Independent and Dependent Events-answers Probability of Dependent Events: P(A, then B) = P(A) * P(B after A) 1/904/90 or 2/445 6/90 or 1/15 4/90 or 2/45 6/90 or 1/15 24/90 or 4/15

38. 12-5 Independent and Dependent Events

39. 12-5 Independent and Dependent Events-answers Dependent, the total number of cards has been reduced by 1 Independent, the possibilities on the second roll are the same as on the first.

40. 12-5 Independent and Dependent Events

41. 12-5 Independent and Dependent Events-answers 8/100 or 2/259/100 12/100 or 3/256/100 or 3/50 20/72 or 5/18 12/72 or 1/6 20/72 or 5/18

42. Probability  Probability - when outcomes are equally likely  Probability of an event = P(event) = # of favorable outcomes # of possible outcomes

44. 1/4 1 3/4 1/6 5/6 0/6; 0

45. Finding Odds  Think of probability as a part/whole = this is called odds - this describes the likelihood of an event Odds in favor of an event = # of favorable outcomes # of unfavorable outcomes Odds against an event = # of unfavorable outcomes # of favorable outcomes

47. 3 to 2; 2 to 3 2 to 3 ; 3 to 2

48. 12-7 Experimental Probability Experimental Probability: probability based on experimental data. Experimental Probability: P(event) = __number of times an event occurs number of times experiment is done

49. 12-7 Experimental Probability

50. 12-7 Experimental Probability-answers 17.6%; 12/68 16.2%; 11/68 13.2%; 9/68 25%; 17/68 77.9%; 53/68 0%; 0/68

51. 12-7 Experimental Probability

52. 12-7 Experimental Probability-answers 1/2 1/8 3/87/8

53. 12-8 Random Samples and Surveys Population: group about which you want information Sample: part of population you use to make estimates about the population. Larger the sample, more reliable your estimates will be. Random Sample: each member of the population has an equal chance to be selected.

54. 12-8 Random Samples and Surveys

55. 12-8 Random Samples and Surveys-answers 320 students 352 students 200 students 192 students

56. 12-8 Random Samples and Surveys

57. 12-8 Random Samples and Surveys-answers Views of people coming out of computer store may not represent the views of other voters. Not a good sample because not random. The city telephone book may cover more than one school district. It would include people who do not vote. Not a good sample, does not represent population. Good sample. People selected at random.