# Contingency Tables

##### New Member
I am doing AS Level Statistics, i just finished S1 and started doing S2 however, my teacher is not that experienced at teaching A level, so therefore, i didn't get what he was talking about !!?

I tried doing it at home but did not get it !!? Can someone please just do a little summary on Contingency Tables plz? :tup:

Last edited:
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#### elnaz

##### Guest
hello
the best thing for learning is book , you can use statistical books for that or
you can search in google also, and there is several site that explain about statistical words.
for e.g:
have been taken of http://davidmlane.com/hyperstat/
Contingency Table

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A contingency table is a table showing the responses of subjects to one variable as a function of another variable. For instance, the following contingency table shows color preference as a function of age (the data are hypothetical). The entries show the number of subjects at each age level choosing a particular color as their favorite. Color preference for blue increases with age whereas color preference for red decreases. The chi square test of independence is used to test the relationship between rows (age) and columns (color preference) for significance.

Favorite Color
Age Red Blue Yellow
4 14 2 4
8 10 4 6
12 5 10 5
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Contingency table
In statistics, contingency tables are used to record and analyse the relationship between two or more variables, most usually categorical variables.

Suppose that we have two variables, sex (male or female) and handedness (right-handed or left-handed). We observe the values of both variables in a random sample of 100 people. Then a contingency table can be used to express the relationship between these two variables, as follows:

right-handed left-handed TOTAL
male 43 9 52
female 44 4 48
TOTAL 87 13 100

The figures in the right-hand column and the bottom row are called marginal totals and the figure in the bottom right-hand corner is the grand total.

The table allows us to see at a glance that the proportion of men who are right-handed is about the same as the proportion of women who are. However the two proportions are not identical, and the statistical significance of the difference between them can be tested with a Pearson's chi-square test, a G-test or Fisher's exact test, provided the entries in the table represent a random sample from the population contemplated in the null hypothesis. If the proportions of individuals in the different columns varies between rows (and, therefore, vice versa) we say that the table shows contingency between the two variables. If there is no contingency, we say that the two variables are independent.

The example above is for the simplest kind of contingency table, in which each variable has only two levels; this is called a 2 x 2 contingency table. In principle, any number of rows and columns may be used. There may also be more than two variables, but higher order contingency tables are hard to represent on paper. The relationship between ordinal variables, or between ordinal and categorical variables, may also be represented in contingency tables, though this is less