1. Agresti/Franklin Statistics, 1 of 33 Enrollment Fall 2005 (all students) ClassificationMenWomenTotal Undergraduate 1,533 (52%) 1,416 (48%) 2,949 Professional*172239 Graduate1,2856981,983 Master505276781 Doctoral7804221,202 Total 2 2,835 2,136 4,971

2. Agresti/Franklin Statistics, 2 of 33 Geographic Origin 3 (Fall 2005) Undergraduates*GraduatesTotal MasterDoctoral Texas 1,532 (51.3%) 4744822,488 Other U.S. 1,320 (44.2%) 1571781,655 International 96 (3.2%) 123521740 Not Designated 40 (1.3%) 272188 Total2,9887811,2024,971

3. Agresti/Franklin Statistics, 3 of 33 Student Demographics (Fall 2005) UndergradGrad #%Master%Doctoral% Architecture1264%749%11% Engineering75125%365%46439% Humanities55919%162%17514% Management--0%47160%--0% Music1284%12316%393% Natural Sciences70423%294%34629% Social Sciences69323%--0%13511% Interdisciplinary211%--0%423% Continuing Studies--0%324%--0% Unclassified61%--0%--0% Total2,9887811,202100%

4. Agresti/Franklin Statistics, 4 of 33 Chapter 1 Statistics: The Art and Science of Learning from Data Learn …. What Statistics Is Why Statistics Is Important

5. Agresti/Franklin Statistics, 5 of 33  Chapter 1 Learn… How Data is Collected How Data is Used to Make Predictions

6. Agresti/Franklin Statistics, 6 of 33  Section 1.1 How Can You Investigate using Data?

7. Agresti/Franklin Statistics, 7 of 33 Health Study Does a low-carbohydrate diet result in significant weight loss?

8. Agresti/Franklin Statistics, 8 of 33 Market Analysis Are people more likely to stop at a Starbucks if they’ve seen a recent TV advertisement for their coffee?

9. Agresti/Franklin Statistics, 9 of 33 Heart Health Does regular aspirin intake reduce deaths from heart attacks?

10. Agresti/Franklin Statistics, 10 of 33 Cancer Research Are smokers more likely than non- smokers to develop lung cancer?

11. Agresti/Franklin Statistics, 11 of 33 To search for answers to these questions, we… Design experiments Conduct surveys Gather data

12. Agresti/Franklin Statistics, 12 of 33 Statistics is the art and science of: Designing studies Analyzing data Translating data into knowledge and understanding of the world

13. Agresti/Franklin Statistics, 13 of 33 Example from the National Opinion Center at the University of Chicago: General Social Survey (GSS) provides data about the American public Survey of about 2000 adult Americans

14. Agresti/Franklin Statistics, 14 of 33 Example from GSS: Do you believe in life after death?

15. Agresti/Franklin Statistics, 15 of 33 Three Main Aspects of Statistics Design Description Inference

16. Agresti/Franklin Statistics, 16 of 33 Design How to conduct the experiment How to select the people for the survey

17. Agresti/Franklin Statistics, 17 of 33 Description Summarize the raw data Present the data in a useful format

18. Agresti/Franklin Statistics, 18 of 33 Inference Make decisions or predictions based on the data.

19. Agresti/Franklin Statistics, 19 of 33 Example: Harvard Medical School study of Aspirin and Heart attacks Study participants were divided into two groups Group 1: assigned to take aspirin Group 2: assigned to take a placebo

20. Agresti/Franklin Statistics, 20 of 33 Example: Harvard Medical School study of Aspirin and Heart attacks Results: the percentage of each group that had heart attacks during the study: 0.9% for those taking aspirin 1.7% for those taking placebo

21. Agresti/Franklin Statistics, 21 of 33 Example: Harvard Medical School study of Aspirin and Heart attacks Can you conclude that it is beneficial for people to take aspiring regularly? Example: Harvard Medical School study of Aspirin and Heart attacks

22. Agresti/Franklin Statistics, 22 of 33  Section 1.2 We Learn About Populations Using Samples

23. Agresti/Franklin Statistics, 23 of 33 Subjects The entities that we measure in a study Subjects could be individuals, schools, countries, days,…

24. Agresti/Franklin Statistics, 24 of 33 Population and Sample Population: All subjects of interest Sample: Subset of the population for whom we have data

25. Agresti/Franklin Statistics, 25 of 33 Geographic Origin (Fall 2005) Undergraduates*GraduatesTotal MasterDoctoral Texas 1,532 (51.3%) 4744822,488 Other U.S. 1,320 (44.2%) 1571781,655 International 96 (3.2%) 123521740 Not Designated 40 (1.3%) 272188 Total2,9887811,2024,971

26. Agresti/Franklin Statistics, 26 of 33 Enrollment Fall 2005 ClassificationMenWomenTotal Undergraduate 1,533 (52%) 1,416 (48%) 2,949 Professional*172239 Graduate1,2856981,983 Master505276781 Doctoral7804221,202 Total 2 2,835 2,136 4,971

27. Agresti/Franklin Statistics, 27 of 33 Majors (Fall 2005) UndergradGrad #%Master%Doctoral% Architecture1264%749%11% Engineering75125%365%46439% Humanities55919%162%17514% Management--0%47160%--0% Music1284%12316%393% Natural Sciences70423%294%34629% Social Sciences69323%--0%13511% Interdisciplinary211%--0%423% Continuing Studies --0%324%--0% Unclassified61%--0%--0% Total2,9887811,202100%

28. Agresti/Franklin Statistics, 28 of 33 Example Format Picture the Scenario Question to Explore Think it Through Insight Practice the concept

29. Agresti/Franklin Statistics, 29 of 33 Example: The Sample and the Population for an Exit Poll In California in 2003, a special election was held to consider whether Governor Gray Davis should be recalled from office. An exit poll sampled 3160 of the 8 million people who voted.

30. Agresti/Franklin Statistics, 30 of 33 What’s the sample and the population for this exit poll? The population was the 8 million people who voted in the election. The sample was the 3160 voters who were interviewed in the exit poll. Example: The Sample and the Population for an Exit Poll

31. Agresti/Franklin Statistics, 31 of 33 Descriptive Statistics Methods for summarizing data Summaries usually consist of graphs and numerical summaries of the data

32. Agresti/Franklin Statistics, 32 of 33 Types of U.S. Households

33. Agresti/Franklin Statistics, 33 of 33 Inference Methods of making decisions or predictions about a populations based on sample information.

34. Agresti/Franklin Statistics, 34 of 33 Parameter and Statistic A parameter is a numerical summary of the population A statistic is a numerical summary of a sample taken from the population

35. Agresti/Franklin Statistics, 35 of 33 Randomness Simple Random Sampling: each subject in the population has the same chance of being included in that sample Randomness is crucial to experimentation

36. Agresti/Franklin Statistics, 36 of 33 Variability Measurements vary from person to person Measurements vary from sample to sample

37. Agresti/Franklin Statistics, 37 of 33 a. To describe whether a sample has more females or males. b. To reduce a data file to easily understood summaries. c. To make predictions about populations using sample data. d. To predict the sample data we will get when we know the population. Inferential Statistics are used:

38. Agresti/Franklin Statistics, 38 of 33 Chapter 2 Exploring Data with Graphs and Numerical Summaries Learn …. The Different Types of Data The Use of Graphs to Describe Data The Numerical Methods of Summarizing Data

39. Agresti/Franklin Statistics, 39 of 33  Section 2.1 What are the Types of Data?

40. Agresti/Franklin Statistics, 40 of 33 In Every Statistical Study: Questions are posed Characteristics are observed

41. Agresti/Franklin Statistics, 41 of 33 Characteristics are Variables A Variable is any characteristic that is recorded for subjects in the study

42. Agresti/Franklin Statistics, 42 of 33 Variation in Data The terminology variable highlights the fact that data values vary.

43. Agresti/Franklin Statistics, 43 of 33 Example: Students in a Statistics Class Variables: Age GPA Major Smoking Status …

44. Agresti/Franklin Statistics, 44 of 33 Data values are called observations Each observation can be: Quantitative Categorical

45. Agresti/Franklin Statistics, 45 of 33 Categorical Variable Each observation belongs to one of a set of categories Examples: Gender (Male or Female) Religious Affiliation (Catholic, Jewish, …) Place of residence (Apt, Condo, …) Belief in Life After Death (Yes or No)

46. Agresti/Franklin Statistics, 46 of 33 Quantitative Variable Observations take numerical values Examples: Age Number of siblings Annual Income Number of years of education completed

47. Agresti/Franklin Statistics, 47 of 33 Graphs and Numerical Summaries Describe the main features of a variable For Quantitative variables: key features are center and spread For Categorical variables: key feature is the percentage in each of the categories

48. Agresti/Franklin Statistics, 48 of 33 Quantitative Variables Discrete Quantitative Variables and Continuous Quantitative Variables

49. Agresti/Franklin Statistics, 49 of 33 Discrete A quantitative variable is discrete if its possible values form a set of separate numbers such as 0, 1, 2, 3, …

50. Agresti/Franklin Statistics, 50 of 33 Examples of discrete variables Number of pets in a household Number of children in a family Number of foreign languages spoken

51. Agresti/Franklin Statistics, 51 of 33 Continuous A quantitative variable is continuous if its possible values form an interval

52. Agresti/Franklin Statistics, 52 of 33 Examples of Continuous Variables Height Weight Age Amount of time it takes to complete an assignment

53. Agresti/Franklin Statistics, 53 of 33 Frequency Table A method of organizing data Lists all possible values for a variable along with the number of observations for each value

54. Agresti/Franklin Statistics, 54 of 33 Example: Shark Attacks

55. Agresti/Franklin Statistics, 55 of 33 Example: Shark Attacks What is the variable? Is it categorical or quantitative? How is the proportion for Florida calculated? How is the % for Florida calculated? Example: Shark Attacks

56. Agresti/Franklin Statistics, 56 of 33 Insights – what the data tells us about shark attacks Example: Shark Attacks

57. Agresti/Franklin Statistics, 57 of 33 Identify the following variable as categorical or quantitative: Choice of diet (vegetarian or non-vegetarian): a. Categorical b. Quantitative

58. Agresti/Franklin Statistics, 58 of 33 Number of people you have known who have been elected to political office: a. Categorical b. Quantitative Identify the following variable as categorical or quantitative:

59. Agresti/Franklin Statistics, 59 of 33 Identify the following variable as discrete or continuous: The number of people in line at a box office to purchase theater tickets: a. Continuous b. Discrete

60. Agresti/Franklin Statistics, 60 of 33 The weight of a dog: a. Continuous b. Discrete Identify the following variable as discrete or continuous:

61. Agresti/Franklin Statistics, 61 of 33  Section 2.2 How Can We Describe Data Using Graphical Summaries?

62. Agresti/Franklin Statistics, 62 of 33 Graphs for Categorical Data Pie Chart: A circle having a “slice of pie” for each category Bar Graph: A graph that displays a vertical bar for each category

63. Agresti/Franklin Statistics, 63 of 33 Example: Sources of Electricity Use in the U.S. and Canada

64. Agresti/Franklin Statistics, 64 of 33 Pie Chart

65. Agresti/Franklin Statistics, 65 of 33 Bar Chart

66. Agresti/Franklin Statistics, 66 of 33 Pie Chart vs. Bar Chart Which graph do you prefer? Why?

67. Agresti/Franklin Statistics, 67 of 33 Graphs for Quantitative Data Dot Plot: shows a dot for each observation Stem-and-Leaf Plot: portrays the individual observations Histogram: uses bars to portray the data

68. Agresti/Franklin Statistics, 68 of 33 Example: Sodium and Sugar Amounts in Cereals

69. Agresti/Franklin Statistics, 69 of 33 Dotplot for Sodium in Cereals Sodium Data: 0 210 260 125 220 290 210 140 220 200 125 170 250 150 170 70 230 200 290 180

70. Agresti/Franklin Statistics, 70 of 33 Stem-and-Leaf Plot for Sodium in Cereal Sodium Data: 0 210 260 125 220 290 210 140 220 200 125 170 250 150 170 70 230 200 290 180

71. Agresti/Franklin Statistics, 71 of 33 Frequency Table Sodium Data: 0 210 260 125 220 290 210 140 220 200 125 170 250 150 170 70 230 200 290 180

72. Agresti/Franklin Statistics, 72 of 33 Histogram for Sodium in Cereals

73. Agresti/Franklin Statistics, 73 of 33 Which Graph? Dot-plot and stem-and-leaf plot: More useful for small data sets Data values are retained Histogram More useful for large data sets Most compact display More flexibility in defining intervals

74. Agresti/Franklin Statistics, 74 of 33 Shape of a Distribution Overall pattern Clusters? Outliers? Symmetric? Skewed? Unimodal? Bimodal?

75. Agresti/Franklin Statistics, 75 of 33 Symmetric or Skewed ?

76. Agresti/Franklin Statistics, 76 of 33 Example: Hours of TV Watching

77. Agresti/Franklin Statistics, 77 of 33 Identify the minimum and maximum sugar values: a. 2 and 14 b. 1 and 3 c. 1 and 15 d. 0 and 16

78. Agresti/Franklin Statistics, 78 of 33 Consider a data set containing IQ scores for the general public: What shape would you expect a histogram of this data set to have? a. Symmetric b. Skewed to the left c. Skewed to the right d. Bimodal

79. Agresti/Franklin Statistics, 79 of 33 Consider a data set of the scores of students on a very easy exam in which most score very well but a few score very poorly: What shape would you expect a histogram of this data set to have? a. Symmetric b. Skewed to the left c. Skewed to the right d. Bimodal