This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
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(BA) = .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” ...
View Full
Document
 Fall '11
 Pavur

Click to edit the document details