ECO220Y1 Study Guide - Midterm Guide: Mutual Exclusivity, Covariance, Univariate
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ECO220Y1 Full Course Notes
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Lecture 1: sampling errors & non sampling errors. Goal to make inferences about population parameter from sample statistics. Inferential conclusion about data not 100% sure: descriptive describes what happened (ex. Class avg: describes sample (data) using statistics, make inferences about population and its parameters using observed data (sample) Population = set of all items of interest (ex. Parameter = descriptive measure of a population (something describes population ex. Sample = subset of the population (ex. Statistics = descriptive measure of a sample (ex. Sampling error, white noise", sample noise", sampling variability" = the purely random differences between a sample and the population that arises b/c the sample is a random subset of the population. As sample size gets larger the sampling error tends to get smaller. Pick 200 out of 60,000; could result in extremely different (due to change: not wrong b/c random sample nothing wrong with survey itself.
