Stats Exam 2

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  1. What is the linear Model
    • ŷ = b+ bx
    • bo is intercept and b1 is slope
    • find this by using least squares method
  2. Lest Squares Method
    • b1Sy/Sx  and b0= Y-bar - b1x̅  
  3. What is a Residual
    • difference between the observed value and what the model predicts
    • e = y-ŷ  Residual = observed - predicted
    • also: y = ŷ + e
  4. Linear Regression
    Way to model the relationship between two quantitative variables with a line
  5. scatterplot
    shows relationship between quantitative variables.
  6. Explanatory Variable
    causal variable on the x-axis
  7. Leverage Point
    • Outlying in X-values
    • is influential
  8. parameter and statistics
    • parameter: unknown population quantities
    • Statistics computed from a sample are used to estimate parameters which describe a population.
  9. Simple Random Sample
    every possible sample of size n has the same chance of being selected
  10. Stratified Random Sample
    divide area into subareas and then take a simple random sample within those subareas
  11. Systematic Random Sample
    involves sampling every kth unit in an ordered list of the units in the population
  12. Cluster Sampling
    random sample of clusters of individuals, and then survey every indidual
  13. Multistage Designs
    situations when using more than 1 sampling mehtod
  14. What are the Sampling Bias's
    • Nonresponse
    • Interviewer Bias - The teacher personally asking students about the class, or only asking certain groups known of having a certain opinion
    • Question Wording
  15. Response Variable
    • Effect variable
    • on the Y-Axis
  16. Observational Study
    • The researcher only observes to collect data
    • no manipulation
  17. Retrospective Studies
    • The response variable is already known
    • the explanatory variable is collected by looking at past histories of subjects
  18. Prospective Studies
    Explanatory Variable is observed about subjects, and then they are followed into the future to see response variables
  19. Experiment
    • Researcher assigns the explanatory variable for each unit
    • a cause and effect relationship can be established
    • if conducted properly the lurking variable can be minimized
  20. Factors and levels
    in an experiment, a factor is usually a categorical explanatory variable, and the levels are the different possible values
  21. elements for a good experiment
    • Necessary: comparison, randomization, Replication
    • Other: Placebo, Blindness
Card Set:
Stats Exam 2
2014-04-10 21:51:14
stats Exam 2
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