1. Chapters 1 & 2 An Overview of Statistics Classifying Data Critical Thinking 1 Larson/Farber 4th ed.

2. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley A Brief History of Statistics The term statistics, derived from the word state, was used to refer to a collection of facts of interest to the state. A systematic collection of data on the population and the economy was begun in the Italian city-states of Venice and Florence during the Renaissance. Slide 2- 2

3. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley A Brief History of Statistics In 1662 the English tradesman John Graunt published a book entitled Natural and Political Observations Made upon the Bills of Mortality Table 1.2, which notes the total number of deaths in England and the number due to the plague for five different plague years, is taken from this book. 3 (John Graunt 1662)

4. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley A Brief History of Statistics Graunt used the London bills of mortality to estimate the city’s population. To estimate the population of London in 1660, Graunt surveyed households in certain London parishes and discovered that, on average, there were approximately 3 deaths for every 88 people. There was roughly 1 death for every 88/3 people. Since the London bills cited 13,200 deaths in London for that year, Graunt estimated the London population to be about 13,200 X 88 / 3 = 387,200 4

5. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Today we refer to Statistics as: The science of collecting, organizing, analyzing, and interpreting data in order to make decisions. 5 ed.

6. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Branches of Statistics Descriptive Statistics: Involves organizing, summarizing, and displaying data. Describes the important characteristics of the data. e.g. Tables, charts, averages, percentages Inferential Statistics: Involves using sample data to draw conclusions or make inferences about an entire population.

7. Example: Descriptive and Inferential Statistics Decide which part of the study represents the descriptive branch of statistics. What conclusions might be drawn from the study using inferential statistics? A sample of Illinois adults showed that 22.7% of those with a high school diploma were obese, and 16.7% of college graduates were obese. (Source: Illinois BRFSS, 2004) 7 Larson/Farber 4th ed.

8. Example: Descriptive and Inferential Statistics Decide which part of the study represents the descriptive branch of statistics. What conclusions might be drawn from the study using inferential statistics? A sample of 471 registered republicans showed that 22% would pick John McCain as the republican nominee for president. (Margin of error: 5%). (Source: USA Today/CNN poll) 8 Larson/Farber 4th ed.

9. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 9 What Are Data? Data can be numbers, names, or other labels, however, without a context a listing of such information is USELESS!! Information coming from observations, counts, measurements, or responses that have been collected.

10. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 10 The “W’s” of Data: To provide context we need the W’s Who What (and in what units) When Where Why (if possible) and How of the data. Note: the answers to “who” and “what” are essential.

11. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 11 Who The Who of the data tells us the individual cases or observational units for which (or whom) we have collected data. Sometimes people just refer to data values as observations and are not clear about the Who. But we need to know the Who of the data so we can learn what the data say.

12. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 12 Who (cont.) Individuals who answer a survey are called respondents. People on whom we experiment are called subjects or participants. Animals, plants, and inanimate subjects are called experimental units.

13. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 13 Data Tables (see p. 7-8) The following data table represents some data that a company like Amazon might collect. What might the context of this data be? Can you pick out the WHO and WHAT of the data displayed in this table? How about the population and the sample?

14. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Definitions Population The collection of all outcomes, responses, measurements, or counts that are of interest. Sample The collection of data from a subset of the population. Census The collection of data from every member of the population.

15. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Example: Identify the population, and whether a census or sample would be done. 1. MSU is doing a study on how many credit hours a MSU student is taking. 2. A fashion magazine gathers data on the price of women’s jeans.

16. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 16 What and Why Variables are the changing characteristics recorded about each individual case. The variables should identify What has been measured. To understand variables, you must Think about what you want to know. We will classify variables in two ways as either CATEGORICAL or QUANTITATIVE

17. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 17 Types of Variables Qualitative or Categorical Variables Consists of attributes, labels, or non- numerical entries. MajorPlace of birth Eye color 17 Larson/Farber 4th ed. Common statistic calculated: percentages

18. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 18 Quantitative Variables Measured with units and answers questions about the quantity of what is measured. AgeWeight of a letterTemperature 18 Larson/Farber 4th ed. Common statistic calculated: averages

19. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 19 BEWARE!! Not all numerical variables can be classified as quantitative variables. For example, consider the following list of zip codes: 07446 082040702810704 These data values of the variable zip codes are certainly Numerical, however, they are labeling locations and do Not have units of measure. Therefore zip code is a Categorical variable!!

20. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 20 What and Why The questions we ask, the What and Why of our analysis, shape what we think about and how we treat the variables. Example: A Consumer Reports article about 98 HDTVs lists each manufacturer, cost, screen size, type,(LCD, plasma or rear projection), and overall performance score (0-100) Identify the Who, Variables, What & Why of this study and include units where appropriate. Label which variables are categorical or quantitative

21. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 21 Where, When, and How We need the Who, What, and Why to analyze data. Certainly the more we know, the more we will understand about our data. When and Where give us some nice information about the context of the data. Example: Consider gathering data on the typical college grad salary. Would you expect a difference in the values if data were collected in the 1970’s and then only last year?

22. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 22 Where, When, and How (cont.) How the data are collected can make the difference between insight and nonsense. Example: consider a policeman collecting data about teenagers speeding by asking a sample of young people if they ever drove faster than 65 mph The first step of any data analysis should be to examine the W’s—this is a key part of the Think step of any analysis.

23. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Uses of Statistics Almost all fields of study benefit from the application of statistical methods Statistics often lead to change

24. Bad Samples Small Samples Misleading Graphs Pictographs Loaded Questions Correlation & Causality Self Interest Study Misuses of Statistics

25. Misuse: Misleading Graphs

26. Misuse: Loaded Questions

27. Misuse: Correlation does not imply Cause and Effect