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  1. categorical data
    values that fall into separate, non-overlapping categories such as marital status or hair color
  2. quantitative data
    values that have measurement units such as dollars, degrees, inches, etc
  3. five-number summary
    minimum value, Q1, the median, Q3, and maximum value
  4. mean
    • x-bar=(sum of values/number of values)
    • not resistant to extreme values
  5. median
    • middle of data set when the data have been ordered
    • resistant to outliers
    • a more appropriate measure of center when outliers are present or distribution is skewed
  6. standard deviation
    • measure of spread (variation)
    • not resistant to outliers
  7. Interquartile Range (IQR)
    • IQR=Q3-Q1
    • gives the spread of the middle 50% of the data
    • resistant to outliers
  8. range
    • maximum-minimum
    • single number
    • extremely sensitive to outlying values
  9. z-scores
    z=(data value-mean)/SD
  10. normal models/empirical rule
    68 - 95 - 99.7%
  11. finding normal percentiles
    • 1. identify the variables and state the problem in terms of the observed variables
    • 2. standardize the values by converting to z-scores
  12. finding percentile (calculator)
    • with z-score: 2nd Distr --> normalcdf (lower bound, upper bound)
    • without z-score: 2nd Distr --> normalcdf (lower bound, upper bound, mean, standard deviation)
  13. explanatory variable
    defines the groups to be compared with respect to values of the response variables
  14. response variable
    the variable you hope to predict or explain. the outcome
  15. correlation coefficient (r)
    • no units
    • requires quantitative variables
    • -1<r<1
    • r=0 represents no correlation
    • r only measures the strength of a linear relationship
    • not resistant to outliers
  16. least squares regression
    • y=bo+b1x
    • y=a+bx
    • bo(a) is the y-intercept
    • b1(b) is the slope in "y-units per x-unit"
  17. residual
    • observed value - predicted value (y-ycap)
    • the sum of the residuals is always equal to 0
  18. performing a simulation
    • 1. identify the trial to be repeated
    • 2. state how you will model the random occurrence of an outcome
    • 3. explain how you will simulate the trial
    • define the response variable
    • 4. run several trials
    • 5. summarize the results across all trials
    • 6. describe what your simulation shows and draw your conclusions about the real world
  19. population
    entire group of individuals that we hope to learn about
  20. sample
    a smaller group of individuals selected from the population
  21. parameter
    • a number that characterizes some aspect of the population such as the mean or standard deviation of some variable of the population
    • Greek letters
  22. statistic
    • values calculated for sample data
    • used to estimate values in the population (parameters)
    • standard letters
  23. simple random sample
    each possible sample of n individuals has an equal chance of selection
  24. stratified random sample
    • population is first broken up into homogeneous groups called strata
    • strata have something in common that effects the response variable
  25. cluster sampling
    divides the population into heterogeneous groups called clusters and then takes an SRS of some of the clusters
  26. bias in sampling methods
    • undercoverage
    • voluntary response bias
    • convenience sample
    • nonresponse
    • response bias
  27. observational study
    researchers observe individuals and record variables of interest but do not impose a treatment
  28. experiment
    • researcher deliberately imposes a treatment
    • must identify at least one response and explanatory variable
    • used to determine a cause-and-effect relationship
  29. block design
    groups based on a certain characteristic that they share that may affect the results of the experiment
  30. matched pairs design
    • a form of block design
    • one subject: receives both treatments
  31. control
    no treatment/traditional treatment/placebo
  32. lurking variables
    variables that we did not think to measure but which can affect the response variable
  33. randomize
    reduces bias by equalizing the effects of lurking variables
  34. replicate
    • should include many subject in a comparative experiment
    • experiment should be designed so that other researchers can replicate our results
  35. single blind
    subjects do not know which treatment group they have been assigned OR those who evaluate the results of the experiment do not know how subjects have been allocated to treatment groups
  36. double blind
    NEITHER the subject nor the evaluators know how the subjects have been allocated to treatment groups
  37. confounding
    cannot separate the effect of a treatment (explanatory variable) from the effects of other influences (confounding variables) on the response variable
  38. trial
    single attempt or realization of a random phenomenon (rolling a pair of dice)
  39. outcome
    the value measured, observed, or reported for each trial (the faces shown on the dice)
  40. sample space
    the set of all possible outcomes
  41. event
    • collection of outcomes
    • usually designated by capital letters
    • ie: A=the probability of rolling a sum of seven when two dice are rolled
  42. the law of large numbers
    the long-run relative frequency of repeated independent events gets closer and closer to the true relative frequency as the number of trials increases
  43. independence
    if the occurrence of one event does not alter the probability that the other event occurs
  44. compliment of event A
    the set of all possible outcomes that are NOT in the event A
  45. disjoint
    cannot occur together
Card Set:
2013-04-27 03:42:54
AP Statistics math exam

AP Statistics exam flash cards
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