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Unformatted text preview: Chapter 5 Contingency Table Different kinds of probabilities ° marginal — P(F), P(JR), - joint — P(F and SR), - OR’s — P(F or SR), ° conditional — P(SR | F),... 0 complement — P(S—R) ,... Important terms: 1) independent (- events that don’t affect each other
2) mutually exclusive (- events that cannot both occur Two Rules 1) P(A or B) = P(A) + P(B) provided A and B are mutually exclusive
2) P(A and B) = P(A) - P(B) provided A and B are independent
NOTE: Both rules apply to two or more events Sections 5.3 and 5.4 - Word problems
Put these into a 2 X 2 contingency table m 100 You are always given three pieces of information, such as:
P(A) = .2, P(B) = .3, and P(B|A) = .6 (- This leads to the values in the above table.
NOTE: Sometimes the third piece of information is P(A and B). Chapter 6
Section 6.1 What is a discrete random variable? X = (counting something) Representing a discrete random variable 1. Make a list 0 with probability _
1 with probability _
X = 2 with probability _ Be sure to bring the two binomial
tables and the Poisson table (Tables A.1, A.2, A.3) to the exam. 10 with probability _ 2. Make a histogram/barchart
3. Use the PMF (probability mass ﬁmction)
Example: P(X = x) is 10Cx(l/2)10 (- PMF for X = (number of heads in 10 coin ﬂips)
Section 6.2 Finding the mean (u) and variance (02) of X
Section 6.3 Binomial random variable (you’ll be provided the values of n and p)
0 PMF - shortcut for the mean (np) and variance [np(l - p)]
0 Bring Table Al and A2
Section 6.4 Poisson random variable (you’ll be provided the value of u) - PMF - Remember: mean = variance
0 Bring Table A3 Note: If X is Poisson, you will see the word “Poisson” ...
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This note was uploaded on 01/13/2012 for the course DCSI 3710 taught by Professor Pavur during the Fall '11 term at North Texas.
- Fall '11