Advanced Higher Geography Statistics. Inferential Statistics 3: The Chi Square Test. Ollie Bray – Knox Academy, East Lothian. Introduction (1). We often have occasions to make comparisons between two characteristics of something to see if they are linked or related to each other.
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The frequency data must have a precise numerical value and must be organised into categories or groups.
The data must be in the form of frequencies
The expected frequency in any one cell of the table must be greater than 5.
The total number of observations must be greater than 20.
Construct a table with the information you have observed or obtained.
Observed Frequencies (O)
(Note: that although there are 3 cells in the table that are not greater than 5, these are observed frequencies. It is only the expected frequencies that have to be greater than 5.)
Eg: expected frequency for old industry in LE1 = (50 x 13) / 92 = 7.07
Eg: Old industry in LE1 is (9 – 7.07)2 / 7.07 = 0.53
Add up all of the above numbers to obtain the value for chi square: χ2 = 15.14.
The number of degrees of freedom to use is: the number of rows in the table minus 1, multiplied by the number of columns minus 1. This is (2-1) x (5-1) = 1 x 4 = 4 degrees of freedom.
We find that our answer of 15.14 is greater than the critical value of 9.49 (for 4 degrees of freedom and a significance level of 0.05) and so we reject the null hypothesis.
‘The distribution of old established industry and food processing industries in Leicester is significantly different.’
The hard bit!
Now you have to look for geographical factors to explain your findings