1. Slide 2-1 Copyright © 2004 Pearson Education, Inc. What Are Data? Data can be numbers, record names, or other labels. Not all data represented by numbers are numerical data (e.g., 1=male, 2=female). Data are useless without their context…

2. Slide 2-2 Copyright © 2004 Pearson Education, Inc. The “W’s” In order to provide context we are interested in the –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.

3. Slide 2-3 Copyright © 2004 Pearson Education, Inc. Example of Data Organized The following data table clearly shows the context of the data presented: Notice that this data table tells us the What (column) and Who (row) for these data.

4. Slide 2-4 Copyright © 2004 Pearson Education, Inc. Who The Who of the data tells us the cases or individuals for which (or whom) we have collected data. –Individuals who answer a survey are called respondents. –People on whom we experiment are called subjects or participants. –Inanimate subjects are called experimental units. Sometimes people just refer to data values as observations.

5. Slide 2-5 Copyright © 2004 Pearson Education, Inc. What Variables are characteristics recorded about each individual. The variables should mention what has been measured. Variables can be classified as categorical (or qualitative) or quantitative. –Categorical examples: sex, race, ethnicity –Quantitative examples: income, height, weight Note: always provide units for quantitative variables.

6. Slide 2-6 Copyright © 2004 Pearson Education, Inc. What (again) Ordinal variables fall in between categorical and quantitative. –Example: the place that a runner finishes in a marathon What is measured tells us the meaning of the data values.

7. Slide 2-7 Copyright © 2004 Pearson Education, Inc. Where, When, How, and Why We need the Who and What of the data to analyze the data. Still, it’s nice to know the other W’s (and H). When and Where give us some nice information about the context. –Example: In investigating incidences of the flu, knowing that the data are from the Flu Epidemic of 1918 tells us something important about the data.

8. Slide 2-8 Copyright © 2004 Pearson Education, Inc. Where, When, How, and Why (cont.) How the data are collected can make the difference between insight and nonsense. –Example: results from Internet surveys are often useless Why reveals the most about our data, giving insight into the Who, What, and How of the data. –Example: Knowing that the King Cola company conducted a taste test of colas might make us skeptical of the results of the study.

9. Slide 2-9 Copyright © 2004 Pearson Education, Inc. What Can Go Wrong? Just because your variable’s values are numbers, don’t assume that it’s a quantitative variable. Always be skeptical—don’t take data for granted.

10. Slide 2-10 Copyright © 2004 Pearson Education, Inc. Key Concepts Variables can be classified as categorical or quantitative. Variables that fall between categorical and quantitative classifications are often called ordinal variables. The context of the data we work with is very important. Always think about the “W’s”—Who, What (and in what units), When, Where, Why (and How)—when examining a set of data.