Topic 1 - Data Overview, Sampling, Data, & describing categorical data

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University of Toronto St. George
Jennifer Murdock

th ECO220Y– LECTURE ONE SEPT 14 Overview, Sampling, Data, & describingcategoricaldata  Context for data values –(5Ws:who,what,when,where,why)  Cases/records: rows in a database;(who)  Respondents: people who answer the survey;  Subjects/Participants: People who are experimented;  Experimental units: inanimate subjects/participants;  Observations: data values;  Variables: columns in a database; characteristics recorded about ei dividual or case; (what)  Database: - Spreadsheet; - Relational database;  Types of Variables(why)  Always consider the question “Why do we need this variable/what do we want to know from it”  Categorical variable: names categories; answer questions about how cases fall into those categoriese.g. area codes; -- Identifier Variable: unique value for each case, for the purposeof identification (e.g. student ID) -- Nominal Variable:only to name categories; -- sample proportion is what matters  Quantitative variable: measures numerical values; tells quantity; -- sample mean is what matters  Other Variable: -- Ordinal variable  Two Types of Information  Quantitativeinformation: - Numerical measurements of a quantity or amount - e.g. A 10% increasein price leads to a 10% decrease in quantity demanded;  Qualitativeinformation: - e.g. An increase in price tends to decrease the quantity demanded;  3 Typesof Data Sets  Cross-sectional: snapshot of different units taken in the same time period; e.g. GDP 2010 for 20 countries;  Time Series: track something over time; e.g. Canadian GDP from 200 until 2010;  Panel (Longitudinal): A cross-section of units where each is followed over time; e.g. GDP of 20 countries from 2000 until 2010;  Sampling – Key terms  Population -- Parameter: number describing a particular population; not practical to obtain; -- Parameters are usually Greek letters (e.g. mu-mean; sigma-standard deviation; );  Sample -- Statistic: number describing a particular sample;  Descriptive statistics: describe a sample (data) using statistics  Inferential statistics: make inference about a population and its parameters using data;  Sample Designs  SRS – Simple Random Sample -- Sampling frame: a list of individuals from which the sample will be drawn;  StratifiedSampling; -- *Strata*  ClusterSampling th  Systematic Samples (e.g. every 10 page)  Multistage Sampling;  Defining the Population Population Sampling Frame  Target Samples  Actual Samples  A Valid Survey  What do I want to know? - Know what you want to know; - Unneces
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