1. Chapter 2: Data Abdullah Syed Fengkai Liao 1 st Period A.P. Statistics

2. What are Data? Data can be numbers, record names, or other labels. Context is important for data values Data Table: This data table clearly shows the context, and tells us the What (column) and Who (row) for these data.

3. The “W’s” To provide context, we need the W’s of the data  Who  What  When  Where  Why  How

4. Who The Who of the data tells us the individual cases 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.  Animals, plants, and inanimate subjects are called experimental units.  In a database, rows are called records. Sometimes people just refer to data values as observations, without being clear of the Who.

5. What and Why The characteristics recorded about each individual are called variables. A categorical variable names categories and answers questions about how cases fall into those categories.  examples: sex, race, ethnicity A quantitative variable is a measured variable (with units) that answers questions about the quantity of what is being measured.  examples: income ($), height (inches), weight (pounds)

6. Counts Count Counting the cases in each category of a categorical variable is not data, but a summary of data. The category is the What, and each counted individual is the Who. Depending on the focus, the unit counted can differ.

7. Identifying Identifiers Identifier variables are categorical variables that only have one individual per category. These variables show the identity of the individual. They include things like names and id codes. Don't analyze identifier variables, but make sure to identify that the variable is indeed an identifier variable. The Why will help determine the correct use for the variable.

8. Where, When, and How When and Where allow us to be in context. Where and When the data is recorded may affect the meaning of the data. How the data is collected allows us to see whether the data is actually accurate. With this extra information, we can know much more about data collection and usage.

9. Question 3: Oil Spills Oil Spills: Owing to several major ocean oil spills by tank vessels, Congress passed the 1990 Oil Pollution Act, which requires all tankers to be designed with thicker hulls. Further improvement in the structural design of a tank vessel have been proposed since then, each with the objective of reducing the likelihood of an oil spill and decreasing the amount of airflow in the event of a hull puncture. To aid in this development, Marine Technology (Jan. 1995) reported on the spillage amount and cause of puncture for 50 recent major oil spills from tankers and carriers.  Who- 50 recent oil spills  Cases- Each of thee 50 recent oil spills is a case  What- Spillage amount and cause of puncture  When- Reported in 1995  Where- Worldwide  Why- To use in designing new tankers  How- Not specified  Variable-Spillage amount  Type- Quantitative  Units- Not specified  Variable- Cause of puncture  Type- Categorical

10. Question 5: Weighing Bears Weighing Bears: Because of the difficulty of weighing a bear in the woods, researchers caught and measured 54 bears, recording their weight, neck size, length, and sex. They hoped to find a way to estimate weight from the other, more easily determined quantities.  Who- 54 bears  Cases- Each bear is a case  What- Weight, neck size, length, and sex  When- Not specified  Where- Not specified  Why- To estimate weight from easier-to-measure variables  How- Researchers collected data on 54 bears they were able to catch  Variable- Weight  Type- Quantitative  Units- Not Specified  Variable- Neck Size  Type- Quantitative  Units- Not Specified  Variable- Length  Type- Quantitative  Units- Not Specified  Variable- Sex  Type- Categorical