6137 Ch 2 Part 1 – Types of variables, Central Tendency.pdf

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ISM 6137 Ch 2 Part 1 1 Chapter 5 Chapter 2 Describing the Distribution of a Single Variable Part 1 Types of variables, Central Tendency 2-2a Populations and Samples A population includes all of the entities of interest in a study (people, households, machines, etc.). Examples All potential voters in a presidential election All subscribers to cable television All invoices submitted for Medicare reimbursement by nursing homes A sample is a subset of the population, often randomly chosen and preferably representative of the population as a whole. 2-2b Data Sets, Variables, and Observations A data set is usually a rectangular array of data, with variables in columns and observations in rows. A variable (or field or attribute ) is a characteristic of members of a population, such as height, gender, or salary. An observation (or case or record ) is a list of all variable values for a single member of a population. Observation/record 2 Observation/record 3 Observation/record 4 Observation/record 1 Variable/field 1 Variable/field 2 Variable/field 3 Variable/field 4 2-2c Types of Data (slide 1 of 5) A variable is numerical if meaningful arithmetic can be performed on it. Otherwise, the variable is categorical . There is also a third data type , a date variable. Excel ® stores dates as numbers, but dates are treated differently from typical numbers. A categorical variable is ordinal if there is a natural ordering of its possible values. If there is no natural ordering, it is nominal . 1 2 3 4
ISM 6137 Ch 2 Part 1 2 Types of Data (slide 2 of 5) Categorical variables can be coded numerically. A dummy variable is a 0–1 coded variable for a specific category. It is coded as 1 for all observations in that category and 0 for all observations not in that category. A binned (or discretized ) variable corresponds to a numerical variable that has been categorized into discrete categories.

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