Stat 332 Cards exam 1

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Anonymous
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Stat 332 Cards exam 1
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2010-02-22 20:26:44
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stat 332 section one exam one BYU provo
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cards for the first test based on his handout.
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  1. Population
    The entire collection of objects or outcomes about which information is sought
  2. Sample
    A subset of a population containing the objects or outcomes that are actually observed.
  3. Simple Random Sample
    of size n is a sample chosen by a method in which each collection of n population items is equally likely to comprise the sample, just as in a lottery
  4. Bias
    difference between sample and population
  5. Conceptual Population
    A population that consists of all values that could possibly be observed, from which a simple random sample comes
  6. Experiment
    An experiment is any procedure that can be infinitely repeated and has a well defined set of outcomes
  7. Sampling Variation
    variation due to performing the experiment
  8. Response Variable
    • Outcome (Dependent Variabel)
    • example: mpg
  9. Factor
    • quantity changed (Independent Variable)
    • example: driving style
  10. Levels
    • exact value of the factor
    • example: windows up/down
  11. sample mean
    Xbar = 1/n { summation (x1 , x2 , ..xn) }
  12. Sampling Variance
    S2= 1/(n-1) { sum 1 to n (xi - xbar) } 2
  13. Sample Standard Deviation
    s= { sqrt (s2) }
  14. Sample Median
    Middle of ORDERED data
  15. Dotplot
    • . .
    • . . . ... . .. . .
    • ________________
  16. Histogram
    the thing with lots of bars and each represents a spread of values like 6-10 or 11-15
  17. box plot
    learn how to make these.. IQR?
  18. Barplot
    do instead of piechart each bar is a percent of total observed, so all are < 1
  19. Sample Space
    Lits of all possible values
  20. Event
    A subset of the Sample Space
  21. Union
    • A or B or Both
    • "A U B"
  22. Intersection
    • A and B
    • "A ∏ B"
  23. Complement
    • Not A
    • Ac
  24. Mutually Exclusive
    Nothing in Common
  25. Axioms of Probability
    • i P(Sample space) = 1
    • ii P(A) ≥ 0
    • iii if A and B are Mutually Exclusive, P(A U B) = P(A) + P(B)
  26. Additive Rule
    P(A U B) = P(A) + P(B) - P( A B)
  27. Number of ways to perform k tasks
    n1, n2, .....nk
  28. Permutation
    • number of ways to choose within group where order matters
    • n! / (n-k)!
  29. Combination
    n! / (k!(n-k)!)
  30. Conditional Probability
    • P(A l B)
    • which is P(A "given" B)
  31. Independence
    P(A l B) = P(A)
  32. Multiplicative Rule
    P( A ∏ B) = P(B) x P(A l B)
  33. Definition of a Random Variable
    quantity that takes on different values with different possibilities numerical
  34. Characteristics of a discrete random variable
    discrete, countably infinite possible values
  35. Probability Mass FUnction
    P(x) = P(X=x) = P(X= specific thing)
  36. Mean of a discrete random variable
    µx = { sum all poss (X x p(x)) }
  37. Variance of a Discrete Random Variable
    sigma2 = {sum. all possible (x-µ)2 x p(x)
  38. Standard Deviation of a Discrete Random Variable
    • sigma = {sqrt (sigma2)}
    • standev disc rand var = sqrt variace disc rand var
  39. Characteristics of a Continuous Random Variable
    Takes on different possible values with different possible probabilities (on the interval)
  40. Probability Density Function
    Probability is Area under P(a < x < b) = {integral a to b ( f(t) dt) }
  41. Mean of a Continuous Random Variable
    µx = integral -infinity to infinity (x f(x)
  42. Variance of Continuous Random Variable
    sigma2 = {integral -inf to +inf [ (x-µ)2 x f(x) dx] }
  43. Standard dev of a cont random variable
    sigma = {sqrt (sigma2)}
  44. Probability Mass Function of Bernoulli (p)
    • P(x) = (1-p if x=0)
    • ( p if x=1)
  45. Mean of Bernoulli
    p
  46. Variance of Bernoulli
    p(1-p)
  47. Characteristics of Binomial (n,p)
    x ~ Binomial (n,p)
  48. Probability Mass Function of Binomial (n,p)
    • P(x) = (nx) px(1-p)n-x if x = 0,1,2....n
    • 0 otherwise
  49. Mean of Binomial (n,p)
    µx = np
  50. variance of Binomial(n,p)
    sigma2x = np(1-p)
  51. Probability mass function of Poisson (lambda)
    • P(x) = e-lambda (lambdax/x!) if x = 0,1,2,....
    • 0 otherwise
  52. mean of Poisson (lambda)
    µx = lambda
  53. Variance of Poisson (lambda)
    sigma2x= lambda
  54. Charactersitics of N (µ,sigma2)
    N is normal, µx = µ ..... sigma2 = sigma2
  55. empirical rule
    • 68% will fall within 1 standev
    • 95% will fall within 2 standev
    • 99.7% will fall within 3 standev
  56. Z-score
    table A.2
  57. compute probabilities of Lognormal (µ, sigma2)
    if take log of lognormal you get normal

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