To understand a regression equation we need to understand what the variables are and what the parameters are. The variables in this case are "Nut Yield" in California, "precipitation" based on annual rainfall data, and "acres" which refers to the number of acres planted.
Next are the parameters. "a" is the intercept. It tells you where the regression equation that you fit to the data will intersect the vertical axis. "b1" is the slope coefficient on precipitation (which is measured in inches). So if it were 1.5 for example then for every inch in rainfall you would expect your yield to increase by 1.5 (tons, I believe but can't remember the unit of measure at the moment). Similarly "b2" is the slope coefficient on acres planted and tells you by how much yield will change based on a one acre change in acres planted.
Okay, I started by getting some rainfall data and nut yield data. the rainfall is for one particular region so if you use this you are assuming the data generalizes across the state.
here is the link to the nut production pdf
You want to begin by specifying your equation. A simple model might suppose that nut yield is a function of rainfall.
If you can get data on acres planted I would just add that to the equation giving you:
I went ahead and entered the data in excel. When you get your data I would just plug it into Excel and follow the same process. If you have a program like Stata or SPSS you could do some more elaborate regression models.
First plot out your data. As we can see yield has been increasing
the following provides an interpretation and discussion of the variables and parameters from a regression equation.