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!
Thanks
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!
Thanks