ECO220_Lecture01_handout.pdf - ECO220 – LECTURE 01 C O U R S E O V E R V I E W S A M P L I N G C AT E G O R I C A L D ATA P B L A N C H E N A Y 1

ECO220_Lecture01_handout.pdf - ECO220 – LECTURE 01 C O U...

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E C O – L E C T U R E C O U R S E O V E R V I E W, S A M P L I N G , C AT E - G O R I C A L D AT A P. B L A N C H E N A Y / / 1. Probability vs. Inferential Statistics In probability, we typically know characteristics of the population. From this, we can compute the likely characteristics of a sample. For instance, I know the exact composition of cards in a deck of cards. From this, I can compute the probability that three cards drawn at random contain two spades and one heart. In (inferential) statistics, it is the other way around: we have infor- mation from a sample; from this we want to infer about the population. Research (in economics and elsewhere) typically relies on statistics: we want to discover something about the world at large, but we only have at our disposal a tiny part of it (the sample). 2. Sampling P : all the elements of interest for a study. E.g. all Toronto inhabitants. P : quantity or characteristic of a population; typically unknown, and usually what we are trying to estimate or infer about. E.g.
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  • Spring '11
  • tanaka

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