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Statistics 110-002 Chapter 2

by: Megan Ruth Simpson

Statistics 110-002 Chapter 2 STAT 110 001

Marketplace > University of South Carolina > Math > STAT 110 001 > Statistics 110 002 Chapter 2
Megan Ruth Simpson

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Chapter 2 lecture notes
Introduction to Statistical Reasoning
Joshua M. Tebbs
Class Notes
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This 2 page Class Notes was uploaded by Megan Ruth Simpson on Thursday September 8, 2016. The Class Notes belongs to STAT 110 001 at University of South Carolina taught by Joshua M. Tebbs in Fall 2016. Since its upload, it has received 6 views. For similar materials see Introduction to Statistical Reasoning in Math at University of South Carolina.

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Date Created: 09/08/16
Statistics 110 Chapter 2 Samples, Good and Bad  How to Sample Badly: -The goal of statistical inference is to use the information in a sample of individuals to describe a larger population of individuals. -Sampling design- The way we select a sample from a population -A Convenience Sample- collects individuals that are the easiest to contact. Convenience sampling is biased. - Voluntary Response Sample- Includes individuals who choose themselves to be included. Voluntary Response is also biased.  Simple Random Samples: To avoid systematic underrepresentation, or bias, use impersonal chance to select individuals. -Simple Random Sampling (SRS)- The sample size or “n” has the same chance of being selected as any other sample of size. Each individual has the same chance of being selected. -Simple random sampling design is unbiased. -Table of Random digits- a list of numbers determined at random. -Sampling Frame- a list of all individuals in the population, (anyone who qualifies to be in a sample)  Final Comments: When selecting a sample of individuals, our goal is to choose one that is representative of the population. Unbiased: Simple Random Sampling Biased: Convenience sampling and Voluntary Response  Other Unbiased sampling designs: -Stratified sampling- individuals are first separated into strata (different groups; gender, race, income class, etc.) Then a SRS is taken from each stratum. This design ensures that individuals from different strata are represented. -Cluster Sampling- Select a sample of clusters (City blocks, high schools, etc.) then sample all individuals in each cluster selected. This is easier to construct that a SRS. -Systematic Sampling- Individuals are selected in a systematic way but in one that does not intentionally bias the results. example: -asking all USC students with ID number ending in “1” to fill out a survey -Selecting every 1000 visitor to a popular web site.


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