Karteikarten
Chapter 7
Karten in diesem Stapel (38)
- 1ElementThe entity that you collect data on
- 2PopulationCollection of all elements
- 3SampleSubset of a population
- 4Sampled Populationpopulation from which the sample is drawn
- 5FrameList of all elements that the sample will be selected from
- 6A Sample Contains only a portion of the population: True or FalseTrue
- 7Samples provide exact exact values of population characteristics: True or FalseFalse, only provide estimates
- 8Why do we select samples?To collect data to answer a research question about a population
- 9Finite SamplesCredit Card numbers, member rosters, account numbers, and other organizational lists
- 10Sampling with Replacementreplacing each sampled element before selecting the next element; not common
- 11Sampling without replacementMost common sampling method
- 12The RAND function in excelgenerates and assigns random numbers for a list with a probability between 0 and 1
- 13Infinite populationSelecting a sample when you do not have the list of all elements
- 14Bank Transactions, help desk phone calls, and production lines are examples ofInfinite population
- 15Random Sample-Each element comes from the population of interest
-Each element is selected independently - 16Point EstimationStatistical Inference; compute a value of a sample statistic that serves as an estimate for a population parameter
- 17An X with a line over itpoint estimator for population mean
- 18Spoint estimator for population standard deviation
- 19p with a line over itpoint estimator for population proportion
- 20Target populationPopulation we want to make inferences about
- 21Sampled PopulationPopulation from which sample is taken
- 22nThe sample size
- 23Nthe population size
- 24Stratified Random Sampling- Population divided into groups of elements called strata
-each element belongs to only one strata
-elements in each strata need to be homogenous - 25Advantage of Stratified Random SamplingIf homogenous, method as precise as simple random sampling but with a smaller sample size
- 26Cluster SamplingPopulation divided into groups of elements called clusters (heterogenous groups) and then a simple random sample is taken of the cluster
- 27Cluster Sampling AdvantageClose proximity of elements can be cost-effective
- 28Cluster Sampling DisadvantageRequires a larger total sample size
- 29Systematic SamplingSample one element for every n/N elements in population
Ex: Selecting every 100th listing in a phone book after selecting the first listing randomly - 30Systematic SamplingSample will be easier to identify than simple random sampling
- 31Convenience SamplingItems included in sample without known probabilities being selected
- 32Example of Convenience SamplingTeacher selecting students randomly for a study
- 33Advantage of Convenience SamplingEasy to use
- 34Disadvantage of Convenience SamplingNo way to tell if sample is proper representation of population
- 35Judgment SamplingPerson most knowledgeable on the study selects elements of a population that he feels are best representation
- 36Use Simple Random, Stratified, Cluster or Systematic sampling becauseThere are formulas to evaluate the accuracy of sample results to see if they are reflective of population
- 37Sampling Distribution of Population MeanProvide probabilty information info on how close sample population mean is to population mean
- 38—
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- Karteikarten am Chapter 7, erstellt von Ashley Shah am 09/10/2016.
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