Can Linear Regression analyses have negative values in the independent variable (x)?

[Joa14]

I have a data that includes temperature in Celsius (y) and forest canopy change in % (x). I wonder if it is statistically correct to plot the forest canopy change with negative values? My idea is to see how much temperature changes as the canopy cover changes.

The code I am using is:

PROC REG DATA=Microclimate CORR;

     MODEL Y=X / CLB P CLM;

     RUN;

Thanks!

[ballardw · Accepted solution]

That is acceptable.

If "canopy" measures either a fixed area of shade generating plant material or percentage of ground covered by such then one would expect a reduction of temperature for a given solar radiation input based on the shade.

Hint: a model without the solar radiation input may generate some odd results because the "canopy" likely doesn't change quickly but temperature can based on the sunshine. (Actually worked a project with ground temperature sensors north, east, west and south of clumps of bunch grass and dealt with such to model ground temperature.)

Remember the underlying generic linear function is a y= mx+b; m is the slope, b an intercept. There is no restriction in general on whether X is positive, negative or 0.

IF a value you measure should not negative values, such as Relative Humidity, then interpretation of a model may require restriction if the dependent value goes negative.