Regression Model is not full rank

Hi all,

I am trying to detect relationship of crop yield at the end of the growing seasson (1 dependent variable) using amount of precipitation in May, June, July, May+June, June+july, Temperature in May etc. The problem is that I can't get p-values for many of these variables because of the following messages:

Note: Model is not full rank. Least-squares solutions for the parameters are not unique. Some statistics will be misleading. A reported DF of 0 or B means that the estimate is biased.

Note: The following parameters have been set to 0, since the variables are a linear combination of other variables as shown.

precJune = 0.37406 * precH - 4.95972 * avairtH - 0.01172 * precMay

So, does anyone knows how to solve the problem? If there is another way to analyze the data, apart from regresion please let me know!



Ambassador to the humans
If you have the amount of precipitation in May and June in your model why are you trying to add the amount in May+June into the model when that is fully covered by May and June separately? If May+June is literally just the May and June amounts added together you get nothing extra from adding that predictor and that is what your software is complaining about.
Thanks for the reply.
I should mention that I tried using the individual months weather data and SAS still finds relationships between my independent variables...

I extracted some information running proc corr but I would like to run regression and quantify any possible relationship..
Do you think I should run several simple linear regressions using only 1 independent variable each time?

Thanks again!