Midterm Notes - Chapter 2 Data Collection Individuals vs...

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Chapter 2: Data Collection Individuals vs. Variables o Individual : the thing we count Ex. students, cars, countries or invoice statements o Variable : some characteristic about the individual Ex. Car-color, price Ex. Countries-population, type of climate Categorical vs. Numerical Data o Categorical (attribute or qualitative) Data: data have values that are described by words rather than numbers Puts individuals into groups Ex. Year in school, color, type of climate, gender, race Data Coding : using numbers to represent categories to facilitate statistical analysis (does not make it numerical) Ex. 1=action, 2=classic, 3=comedy, etc. Binary Data : has only two values, indicating presence (1) or absence (0) of a characteristic of interest Ex. Employment: 1=employed, 0=unemployed o Numerical Data (quantitative): data arise from counting, measuring something, or from some kind of mathematical operation Assigns numbers for use with appropriate arithmetic Ex. GPA, population, price, number of errors Discrete : variable with countable number of values that can be represented by an integer Continuous : variable that can have any value within an interval Rounding can turn continuous data into discrete data Level of Measurement
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o Nominal : categorical data, no ranking Merely identify a category Ex. Male, female o Ordinal : categorical data, ranked values Ex. How often do you ski? ---never, rarely, sometimes, often o Interval : numerical data, ranked values, absence of a meaningful zero is a key characteristic Not only ranked but also has meaningful intervals between scale points Ex. Temperature (zero doesn’t mean anything) Likert Scale : while value may not be numerical, intervals between are consistent Very good, good, ok, bad, very bad Has to be an odd number o Ratio : numerical data, ranked values, meaningful zero (lack of variable being measured) Ex. Profit of a company (zero profit means something) Time Series vs. Cross-Sectional o Time Series : data collected across time If each observation in the sample represents a different equally spaced point in time (years, months, days) Periodicity : the time between observations o Cross-Sectional Data : data collected at a single point in time If each observation represents a different individual unit (a person, firm, geographic area) at the same point in time For this, we are interested in variation among observations or relationships Population vs. Sample o Population : set of all individuals who can answer questions o Cant pull everyone in a population so you use a sample
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o Sample : involves looking only at some items selected from the population o Census : an examination of all items in a defined population Parameter vs. Statistic o Parameter : any measure that describes an entire population The value is fixed (but often unknown) as long as the pop. Stays the same Ex. Average salary of all Americans, no one can know exactly o Statistic : any measurement computed from a sample
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