# Week 3-4.pptx - Chapter 6 Sampling and Estimation Outline...

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Chapter 6 Sampling and Estimation
Sampling and Estimation Central Limit Theorem Sampling distribution of of Confidence intervals for for Outline
Example – Average Spending Among Young Adults in US
Example – Average Spending Among Young Adults in US
Statistics revolves around using a sample to make statements about a population Population – What we’re interested in Sample – What we’re able to get data from Samples only provide estimates of population parameters We never know the truth! Want “good” samples that provide “good” estimates What does this mean? Sampling and Estimation
Good estimates should be Unbiased – estimate is expected to equal what’s it’s estimating Consistent – estimate should get closer to the truth as sample size increases Having a representative sample is very important Bad sample = bad estimates Estimation
Suppose I am interested in estimating the average home price in Coral Gables. What kind of sample would I want? How would I get it? What would be a good/bad idea? Example
Each item is sampled entirely by chance Each item has equal probability of being sampled Good sampling technique, most common, easy to implement Other techniques do exist: Stratified sampling Clustered sampling Systematic sampling Convenience sampling (generally not good) Judgement sampling (generally not good) Simple Random Sampling
Estimation – use sample to estimate a population parameter. Sample mean ( estimates the population mean ( Sample standard deviation () estimates the population standard deviation ( Sample proportion () estimates population proportion (. Estimation
Estimates aren’t perfect Sampling (statistical) error Sample is only a subset of the population Sampling error can be minimized with larger samples Nonsampling error Arises from biased samples Poor sampling design or inadequate data reliability Cannot be remedied with larger samples Sampling Error