Undergrad Determining functional relation of two dependant variables

Click For Summary
The discussion focuses on determining the functional relationship between temperature and conductivity over time, with both variables being dependent. The user seeks methods for analysis since traditional regression techniques assume one independent variable. They note the challenge posed by the undetermined variation in temperature. A suggestion is made to use techniques like loess combined with cubic splines to estimate the relationship without needing to control one variable. The conversation highlights the need for appropriate methodologies in analyzing correlated datasets with both variables dependent.
fsonnichsen
Messages
61
Reaction score
5
I have a pair of correlated datasets that I collected in the lab for temperature and conductivity of a solution vs time. I want to determine the functional relation between the two. (see attached plot-an interesting lead/lag in the phase difference).
If I were trying to determine this relation using for example a carefully controlled temperature I would just use a regression against some order of polynomial on the temperature (the relation is almost linear). However in the present case the temperature varies in an undetermined fashion.
There is a lot of literature out there for doing this when one variable is independent but I cannot find something for both variables dependent on some other parameter (time here). What is the method for doing this and perhaps some texts describing this (or matlab/octave routines for that matter)
Thanks
Fritz
 

Attachments

Last edited:
Physics news on Phys.org
I have seen plenty of analyses that estimate relations between variables from observations where both variables were random. I am not aware of any reason for wanting one of the variables to be controlled, other than that where that is the case one can choose values for that variable in such a way as to eliminate sparsely-covered regions in the range of interest. Is there any reason not to just use a technique such as loess together with cubic spline?
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

Similar threads

  • · Replies 3 ·
Replies
3
Views
3K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 16 ·
Replies
16
Views
2K
Replies
13
Views
2K
  • · Replies 2 ·
Replies
2
Views
4K
  • · Replies 1 ·
Replies
1
Views
973
  • · Replies 4 ·
Replies
4
Views
6K
Replies
3
Views
2K
  • · Replies 13 ·
Replies
13
Views
2K