Hello,
I am wondering what the best statistical test to use for my research is. My research question is looking at different rooms in a building with a green roof, trying to find if there is a significant difference in temperature between rooms under the green roof, and not under the green roof. My data consists of date on the x-axis, and time on the y-axis. What would be the best way to test for significance between the temperatures of the rooms over the testing period? I tried using one-way ANOVA, and then Tukey's HSD test, and got some reasonable results, but I am not sure if I chose the right test.
Upon further investigation, it looks like perhaps I should use the "repeated measures ANOVA" test. Does anyone know how to do this in R? Also, am I able to perform the Tukey's HSD test on this? I was told I have time series data, but I am not sure what to do with it.
I am also quite confused on the whole topic of whether variables are related or independent. I would think my rooms are independent because they are separate rooms? But also, each room is tested over multiple days... I am a novice at statistical tests!
I attached an image of my data below for reference.
Thank you!
As far as I see with temperature (just like weather prediction) should not be dependent for too long(since you have a time series) .
Statistically , you can do a lot such as looking at auto-correlation
E.g: https://campus.datacamp.com/courses/introduction-to-time-series-analysis/correlation-analysis-and-the-autocorrelation-function?ex=9#skiponboarding
Good link: looks like cloud (so you can try there to check the workings)
R is easy , hugggge documentation is available online ...
Since you are looking at continuous variables, any of the numerical test for means should work. If you are looking at comparing 2 variable use TTest; 3 or more ANOVA.
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