Editing Final Report/Thesis 2015 (section)

===Chi-Squared Test Explanation===
The ''chi-squared test'' is a statistical test commonly used to compare observed data with expected data. It requires than there is no relationship between the observed data and expected data. It means the observed data should not affect the expected data and they are independent respectively. <ref> N Turner, “Chi-squared test” Journal of clinical nursing, Vol.9 (1), pp.93 2000. </ref>.

The ''chi-squared test'' a ‘goodness of fit’ test, meaning it measures how closely one group of data is related to another. Since it is a non-parametric test, it doesn’t care about distribution of samples. It means there is no requirement that expected and observed data should follow a normal distribution <ref> N. Balakrishnan et al., Chi-squared Goodness of Fit Tests with Applications [online]. Available: http://www.sciencedirect.com.proxy.library.adelaide.edu.au/science/book/9780123971944 </ref>.

[[File: Technical_Background_Chi-squared.png |thumb|1000px|centre|'''Fig. 4:''' Chi-squared example]]

Figure 4 is a simple example to show how our group is to use the ''chi-squared test'' for Task 1.  Firstly, we can calculate the chi-squared value for the letter A by using the equation on the right of Figure 4. The observed value is the count of letter A in the Somerton Man code, the expected value is the total number of letters in Somerton Man code which is 44, multiplied by the frequency of letter A in the base text language used.  This is repeated to calculate the chi-squared value for the rest of the letters from B to Z.  Finally, the chi-squared value for all letters are summed. This is the chi-squared value between the base text language and Somerton Man code. Lower Chi-squared values mean the Somerton Man code is more likely to be this language.