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typo for comparison
<pre>dataset <- read.csv("filename.csv") #generates a table called dataset with your values</pre>
==Single ComparasonsComparisons==Don't forget to adjust these p-values for multiple comparasons comparisons if you are doing more than one test.
===If you have 2 groups you want to compare===
===If you are testing one variable with more than one value (One Way ANOVA)===
Not if you are comparing 2 groups to control, but if you are comparing three groups internally. For example this might be Normal Diet, High Fat Diet, High Protein Diet. Note that if you do this with just two groups, the result should be the same as a t-test.
* Using R, providing data is formatted in a dataframe named '''dataset''' with columns '''group''' and '''values''' (see http://stat.ethz.ch/R-manual/R-patched/library/stats/html/aov.html). The first step is to do an [http://en.wikipedia.org/wiki/Analysis_of_variance ANOVA], then depending on if the results of this comparason comparison are significant, move on to post-hoc tests such as [http://en.wikipedia.org/wiki/Tukey%27s_range_test TukeyHSD]:
<pre>
fit.aov <- aov(values ~ group, data=dataset) #generates an object names fit.aov
TukeyHSD(fit.aov)
</pre>
This will generate all possible pairwise comparasons comparisons between your groups
====No Main Effect====
TukeyHS(wt.fit)
</pre>
This will tell you, separate from the interaction, whether each pairwise comparason comparison is significant. You will have to repeat this by re-doing subset with each genotype and diet value as needed.
==Correlations==
coming later...
This is when two variables are correlated rather than one of them being discreet
