STAT200 Lecture Notes - Lecture 2: Sampling Frame, Non-Sampling Error, Multistage Sampling

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salmoncrow85

5 Sep 2016

School

University of Delaware

Department

Statistics

Course

STAT200

Professor

Krishnan Palaniappa

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Document Summary

A simple random sample of n measurements from a population is a subset of the population selected in such a manner that every sample size of n from the population has an equal chance of being selected. Every individual of a population in a random sample also has an equal chance of being selected. Each selected sample in a simple random sample represents the population as a whole. A simulation is a numerical facsimile or representation of a real-world phenomenon. Stratified sampling is when the population is divided into at least two distinct strata. Cluster sampling is a method in which the demographic area is divided into sections. The sections are then randomly selected in clusters: each individual in the selected cluster is included. For a population that is very large or geographically spread out, samples are constructed through a multistage sampling design: large samples are selected which are then broken down into smaller blocks.

Related Questions

Explain the difference between a stratified sample and a cluster sample. (Select all that apply.)

(1) In a cluster sample, every sample of size n has an equal chance of being included.

(2) In a stratified sample, every sample of size n has an equal chance of being included.

(3) In a stratified sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

(4) In a cluster sample, random samples from each strata are included.

(5) In a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

(6) In a cluster sample, the only samples possible are those including every kth item from the random starting position.

(7) In a stratified sample, random samples from each strata are included.

(8) In a stratified sample, the only samples possible are those including every kth item from the random starting position.

redrabbit701

Explain the difference between a stratified sample and a cluster sample. (Select all that apply.)

1) In a stratified sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

2) In a stratified sample, random samples from each strata are included.

3) In a cluster sample, the only samples possible are those including every kth item from the random starting position.

4) In a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

5) In a cluster sample, random samples from each strata are included.

6) In a stratified sample, every sample of size n has an equal chance of being included.

7) In a stratified sample, the only samples possible are those including every kth item from the random starting position.

8) In a cluster sample, every sample of size n has an equal chance of being included.

grayotter535

STAT200 Lecture Notes - Lecture 2: Sampling Frame, Non-Sampling Error, Multistage Sampling

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