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## Stats 201

by: Victoria Notetaker

49

0

1

# Stats 201 Stat 201

Victoria Notetaker
CSU

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Lecture 11 and 12
COURSE
General Statistics
PROF.
Xiaowen Hu
TYPE
Class Notes
PAGES
1
WORDS
CONCEPTS
KARMA
25 ?

## Popular in Statistics

This 1 page Class Notes was uploaded by Victoria Notetaker on Wednesday February 24, 2016. The Class Notes belongs to Stat 201 at Colorado State University taught by Xiaowen Hu in Spring 2016. Since its upload, it has received 49 views. For similar materials see General Statistics in Statistics at Colorado State University.

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Date Created: 02/24/16
Conditional Probability: probability of an event, given that another event has occurred P(A| B) is the probability of A, given that B has occurred P(A| B) is not the same as P(B|A) If events are independent then P(A)=P(A|B) and P(B)=P(B|A) A contingency table (two way table) is a visual representation of the relationships between the variable. They make probabilities easier to figure out. Levels of the explanatory variables are the rows of the table and response variables are the columns Risk=Number in category/Total number in group We can use a contingency table to calculate Relative Risk= risk in category 1/ risk in category 2 Baseline risk Percent change in risk= (relative risk – 1) 100% Positive means risk increases Negative means risk decreases If relative risk is 1 then the percent change is 0% Odds of event A= # of observations in A category/ # of observations not in A category Also written as P(A)/ 1-P(A) Odds are usually “to 1” so if your answer is 3 the odds are 3 to 1 If odds are less than 1 outcome is more likely than not If odds are greater than 1 outcome is not as likely Odds equal to mean means even split between possibilities Odds Ratio (OR)=Odds A/Odds B Also written as (P(A)/1-PA)) / (P(B)/1-P(B)) Odds ratio greater than 1 then the outcome of interest is more likely Odds ratio less than 1 then the outcome of interest is not as likely Odds ratio equal to one then the outcome is equally likely in both categories Confounding or lurking variable affects the response variable and is also related to the explanatory variable Simpsons paradox occurs when a relationship appears to switch directions depending on whether or not a confounding variable is considered

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