my background is being an applied mathematician with some basic statistic background.
I have a dataset containing a metric random variable whose value is to be explained by the categorical variable. The dependent variable contains measured values.
The categorical variable has three values A,B,C. Its influence is to be tested; it is nominally scaled. In my data set, I have four repetitions per year for each characteristic and I have the whole thing for 10 years.
My limited knowledge now says that if I only had one year, I would use ANOVA, but due to the four repetitions, I probably don't fulfil the requirements.
Then I would look at one of the many Wilcoxon tests to see if there are differences between A,B and C. More precisely, I need something for unpaired data and three characteristics, i.e. Kruskal-Wallis test?
Now to my questions
-) how do I check the ANOVA assumptions if the year clearly clearly (by eye ;) ) has an influence on the dependent variable. That bends my head a little right now
-) I hope the previous questions and thoughts were understandable. My data looks like the following example (less normally distributed ;) ). How do I test for difference depending on A,B,C
year A,B,C y
1 A 0.456405
1 A 0.848563
1 A 0.925366
1 A 0.962574
1 B 0.0135936
1 B 0.18973
1 B 0.888734
1 B 0.895592
1 C 0.569717
1 C 0.582822
1 C 0.629108
1 C 0.980864
2 A 0.342965
2 A 0.424771
2 A 0.583502
2 A 0.674631
... ... ...
10 C 0.0264971
10 C 0.553408
10 C 0.60247
10 C 0.955483
Many thanks for reading tips/links
Kind regards
PS: the link in that answer is dead ANOVA type for dependent samples through time
PPS: I had a beatiful LaTeX-Table for my data-set but after login in, it displays it in one row :( therefore plain text