This preview shows pages 1–11. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: STA 100 Lecture 9 Paul Baines Department of Statistics University of California, Davis January 24th, 2011 Admin for the Day I Homework 1 can be picked up today (in class or outside my office) I Midterm: Wednesday, Jan 26th, in class I No R or R Commander knowledge needed for this midterm I Closed book one doublesided page of notes + calculator I Extra office hours: Mon 9.3011.30am, Tues 10.0011.00am Admin for the Day I Homework 1 can be picked up today (in class or outside my office) I Midterm: Wednesday, Jan 26th, in class I No R or R Commander knowledge needed for this midterm I Closed book one doublesided page of notes + calculator I Extra office hours: Mon 9.3011.30am, Tues 10.0011.00am References for Today: Rosner, Ch 4.14.12 (7th Ed.) References for Wednesday: Everything so far (Midterm) Topics for Today 1. Probability for discrete data 1.1 Genetics Example 1.2 Binomial Distribution 1.3 Probability Mass Functions 1.4 Random Variables 1.5 Mean and Variance 1.6 Computing Binomial Probabilities Recap: Useful Rules Recall: AND = , OR = , GIVEN =  . Rule/Definition Formula Mutually Exclusive: P ( A or B ) = P ( A ) + P ( B ) Exhaustive: P ( A or B ) = 1 Addition Rule: P ( A or B ) = P ( A ) + P ( B ) P ( A and B ) Complement: P ( A c ) = 1 P ( A ) Independence: P ( A and B ) = P ( A ) P ( B ) General AND: P ( A and B ) = P ( A ) P ( B  A ) Bayes Rule: P ( B  A ) = P ( A B ) P ( A ) = P ( B ) P ( A  B ) P ( B ) P ( A  B )+ P ( B c ) P ( A  B c ) Brief Recap A couple of common things that have come up: I Rule of thumb: SD Range/5 I RightSkew: Mean > Median, Median closer to lower quartile I LeftSkew: Mean < Median, Median closer to upper quartile I Symmetric: Mean Median, Median half way between lower, upper quartiles I 0thPercentile is the minimum value (by convention) I 100thPercentile is the maximum value I IQR = Upper Quartile  Lower Quartile I Outlying values: LQ  1.5*IQR, UQ + 1.5*IQR Types of Data Remember the different types of data? Q: All the probability we have seen so far has been suitable for what type of data? Types of Data Remember the different types of data? Q: All the probability we have seen so far has been suitable for what type of data? A: Discrete data mainly binary and k out of n data. Examples: disease/no disease, guilty/not guilty, heads/tails, dice rolls, # of ginger children out of n . Today we add some more types of data to that list: count data. Genetics Recap Q: With two ( R , r ) parents, each child has a 1 / 4 probability of being a ginger. If the parents have 2 children what is the probability that they will both be gingers? Genetics Recap Q: With two ( R , r ) parents, each child has a 1 / 4 probability of being a ginger. If the parents have 2 children what is the probability that they will both be gingers?...
View
Full
Document
This note was uploaded on 03/09/2011 for the course STAT 100 taught by Professor drake during the Spring '10 term at UC Davis.
 Spring '10
 DRAKE
 Statistics

Click to edit the document details