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2010-04-06 02:57:56

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  1. Discrete Random Variable
    the set of all possible values is at most a finite or a countably infinite number of possible values
  2. Countinuous Random Variable
    takes on values at every point over a given interval
  3. Required for a discrete probability function
    probabilities are between 0 and 1: total of all probabilities equals 1
  4. Binomial Distribution
    exactly two possible outcomes: success and failure
  5. POisson Distribution
    describes a process that extends over time space, on any well defined unit of inspection
  6. uniform distribution
    • uniformly distributed
    • anything outside the box = 0
  7. Normal distribution
    • exhibits the following characteristics: it is continuous distribution, symmetric about the mean, asymptotic
    • to the horizontal axis, unimodal, is a family of curves, area under the curve is 1, it is bell shaped.

    sample size greater than 30
  8. Central Limit theorem
    • assumption of normality
    • sample distribution will be normal regardless of true population distribution
  9. Sampling Reasons
    highly precise, less costly, and less time consuming
  10. sampling error
    • sample mean - population mean
    • the greater the sample size the less probability for error
  11. Point estimate
    the single value of a statistic calculated from a sample which is used to estimate a population parameter
  12. interval estimate
    a range of values calculated from a sample statistic(s) and standardized statistics, such as the Z
  13. Sampling techniques
    nonstatistical sampling: convenience, judgement

    statistical sampling: simple random, systematic, stratified, cluster
  14. statistical sampling
    • items of the sample are chosen based on known or calculated probabilities
    • simple random
    • stratified
    • Systematic
    • Cluster
  15. simple random sampling
    equal chance of being selected
  16. stratified random sampling
    divide population into subgroups (strata) according to some common characteristics
  17. cluster sampling
    divide population into several clusters, simple random sample of clusters