EXAMPLES: balance in your checking account, minutes remaining in class, or number of children in a family. 8 Quantitative Variables - Classifications Quantitative variables can be classified as either discrete or continuous . A. Discrete variables : can only assume certain values and there are usually “gaps” between values. EXAMPLE : the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,…,etc). B. Continuous variable can assume any value within a specified range. EXAMPLE: The pressure in a tire, the weight of a pork chop, or the height of students in a class. 8
Summary of Types of Variables 9 MONEY as Continuous Data By definition (for this course) a monetary value (i.e. money ) is classified as continuous : z Income z Rent z Purchase Price The only time money is considered discrete is if you are counting a number of things : z Number of silver dollars z Number of pennies in your pocket I will not try to trick you
Four Levels of Measurement Nominal level - data that is classified into categories and cannot be arranged in any particular order. EXAMPLES: eye color, gender, religious affiliation. Ordinal level – data arranged in some order, but the differences between data values cannot be determined or are meaningless. EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4. Interval level - similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point. EXAMPLE: Temperature on the Fahrenheit scale. Ratio level - the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement. EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer’s representatives per month. 9 Nominal-Level Data Properties: 1. Observations of a qualitative variable can only be classified and counted . 2. There is no particular order to the labels. 10
Identifying Categories Completely For categories to be sufficient (e.g. for use when setting up a frequency table) the categories must Mutually Exclusive and Collectively Exhaustive z Categories are mutually exclusive if an observation can only belong to one of the categories. z Categories are collectively exhaustive if every possible observation can be matched to an appropriate category. Ordinal-Level Data Properties: 1. Data classifications are represented by sets of labels or names (high, medium, low) that have relative values .