Hi Bora Beran ,
Thanks for sharing the twbx for the LOD calculations. I was looking at the t-critical calculations. The calculations uses SUM(1) -2 . I would really appreciate if you can explain why we are doing df -2 for the calculations.
If anyone else knows the answer please feel free to jump in.@
Bora no longer works at Tableau so he likely won't reply.
This might be too simple but hope help a bit.
The term degrees of freedom refers to the number of scores within a data set that are free to vary. In any sample with a fixed mean, the sum of the deviation scores is equal to zero. If your sample has an n equal to 10. The first 9 scores are free to vary but the 10th score must be a specific value that makes the entire distribution equal to zero. Therefore in a single sample the degrees of freedom would be equal to n - 1. The degrees of freedom for a correlation is slightly different because n equals number of pairs not simply sample size. Therefore, the degrees of freedom for a correlation in n - 2. So to calculate the degrees of freedom you simply take the number of pairs and subtract two. For our data set of depression and self-esteem scores the degrees of freedom are calculated the following way: