Chapter 10 - Part II

# Chapter 10 - Part II - Advantages of 2 χ statistic over z...

This preview shows pages 1–4. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Advantages of 2 χ statistic over z statistic : A statistic can only be used to compare a estimate to its hypotheses value i.e for hypotheses like : H : or H : This is only possible in tables as we saw above. For contingency tables with more than rows and/or columns, we need more than hypotheses to describe the association. Eg : Suppose we want to compare the effectiveness of Advil, Tylenol and Excedrin in reducing headaches. Our contingency table would then look like : Medicine Headache ? Y N Advil p 1 1- p 1 Tylenol p 2 1- p 2 Excedrin p 3 1- p 3 Here is the population proportion of patients taking Advil whose headache was reduced. Similarly for p 2 and p 3. Here the explanatory variable ( ) has categories corresponding to the three medicines used. So, the null hypotheses for independence will be H : We can rewrite this hypotheses as H 01 : and H 02 : Now, in order to test H 01 and H 02 we have to use z statistics (one for each). On the other hand, only 2 χ statistic (with 2 df) will be enough to test this . So, crudely speaking, statistics are easier to use when we have large contingency tables. Limitations of the Chi-square test : We have seen that a p – value corresponding to a...
View Full Document

## This note was uploaded on 04/18/2008 for the course EXP 3116 taught by Professor Fasig during the Spring '08 term at University of Florida.

### Page1 / 8

Chapter 10 - Part II - Advantages of 2 χ statistic over z...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online