Hey larrikinlover.
What is your specific model that you supplied to R?
Thank you for taking the time to read this!!
I am really stuck with a regression I am doing for my dissertation. Basically it is the categorical variable that is confusing me. The categorical variable 'industry' has three levels - yes, no, or next (indicating whether there is or isnt industry, or whether there is industry in the next state) I also have two other independent variables. My dependent variable is Forest Loss.
When I run the regression in R industrynext 'disappears' (is this something to do with it being a reference var? I really dont know). industryno is significant and industryyes is not.
How would I interpret industryyes and industryno, and what/ where is industrynext??
I hope this has made sense!!
Thank you for any help.
Hello my model is -
lm(ForestCoverKm2~Industry+CitySize+DistanceBelem+ FedRoad)
And the output is -
Residuals:
Min 1Q Median 3Q Max
-20416 -6273 -249 2585 124963
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.308e+03 2.630e+03 -3.159 0.00195 **
Industryno 4.733e+03 3.471e+03 1.364 0.17489
Industryyes 5.391e+03 2.794e+03 1.930 0.05571 .
CitySize 2.027e-03 1.130e-02 0.179 0.85793
DistanceBelem 2.357e+01 4.539e+00 5.194 7.29e-07 ***
FedRoad 5.535e+03 2.607e+03 2.124 0.03550 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 14410 on 137 degrees of freedom
Multiple R-squared: 0.2518, Adjusted R-squared: 0.2245
F-statistic: 9.222 on 5 and 137 DF, p-value: 1.382e-07
You need to setup of dummy variables for categorical regression.
Set up two dummy variables where you have:
A B
0 0 - First level
1 0 - Second level
1 1 - Third Level
where A and B are dummy variables with each level being a realization of a particular category.
Then each estimated parameter corresponds to the average number of realizations for that category.
Have you taken a course or read up on Generalized Linear Models/Linear Models before?