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

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University of Toronto St. George

Economics

ECO220Y1

Jennifer Murdock

Fall

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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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