1. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. Chapter 2 Exploring Data with Graphs and Numerical Summaries Section 2.1 Different Types of Data

2. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 2 A variable is any characteristic observed in a study. Examples: Marital status, Height, Weight, IQ A variable can be classified as either  Categorical (in Categories), or  Quantitative (Numerical) Variable

3. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 3 A variable can be classified as categorical if each observation belongs to one of a set of categories: Examples:  Gender (Male or Female)  Religious Affiliation (Catholic, Jewish, …)  Type of Residence (Apartment, Condo, …)  Belief in Life After Death (Yes or No) Categorical Variable

4. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 4 A variable is called quantitative if observations on it take numerical values that represent different magnitudes of the variable. Examples:  Age  Number of Siblings  Annual Income Quantitative Variable

5. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 5 For Quantitative variables: key features are the center and spread (variability) of the data. For Categorical variables: a key feature is the percentage of observations in each of the categories. Main Features of Quantitative and Categorical Variables

6. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 6 A quantitative variable is discrete if its possible values form a set of separate numbers, such as 0,1,2,3,…. Discrete variables have a finite number of possible values. If you COUNT them, it is a discrete quantity “How many ….. ?” usually produces discrete data Discrete Quantitative Variable

7. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 7 A quantitative variable is continuous if its possible values form an interval. Continuous variables have an infinite number of possible values. If you MEASURE it, then it’s a continuous variable “How much …. ?” usually produces continuous data Continuous Quantitative Variable

8. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 8 Ordinal Categorical Variable Data is words, but there is an ORDER or progression to the choices. EXAMPLES: ColdStone: Like it, Love it, Gotta have it Opinion Survey: Strongly disagree, disagree, neutral, agree, strongly agree

9. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 9 Nominal Categorical Variable Data is words, with NO obvious or natural ORDER. Favorite flavor: vanilla, chocolate, pistachio, rocky road What city were you born in?: Phoenix, Glendale, Tuscaloosa, Atlanta, Boston

10. Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 10 The proportion of the observations that fall in a certain class or category is: Frequency of that class Sum of all frequencies The percentage is the proportion multiplied by 100. Proportions and percentages are also called relative frequencies. Proportion & Percentage (Relative Frequencies)