# Stat 332 Cards exam 1

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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)
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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 Author: Anonymous ID: 7767 Filename: Stat 332 Cards exam 1 Updated: 2010-02-23 01:26:44 Tags: stat 332 section one exam one BYU provo Folders: Description: cards for the first test based on his handout. Show Answers:

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