This is what I would like to know, due to some logical problem behind!
I have a model as:
Crown radius = Diameter at breast height + Location
DBH is quantitative, like 30cm, 40cm... Location is Edge or Forest I understand if I use the summary function, Edge = 0 and Forest =1 as dichotomous dummy variable. I get the following summary:
lm(formula = CR ~ DBH + Location, data = TA) Residuals: Min 1Q Median 3Q Max -1.77367 -0.40927 0.08199 0.55866 1.54157 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.778495 0.349110 5.094 9.88e-06 *** DBH 0.055476 0.009065 6.120 3.90e-07 *** LocationF 0.712704 0.288170 2.473 0.018 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.8094 on 38 degrees of freedom Multiple R-squared: 0.6402, Adjusted R-squared: 0.6212 F-statistic: 33.8 on 2 and 38 DF, p-value: 3.678e-09
Hence both variables are signficant. Now the question would be how to extract and interpret the regression equation. In my understanding it would be:
CR = 0.05*DBH + 0.71*Location + 1.78, when Location is 0 = Edge and 1 = Forest.
This strongly contradicts my expectation, indicating that trees in the forest would have a greater crown radius than trees at the edge.
Don't want to go in the details, but do I interpret the output correct?
Is it possible that the estimate of LocationF in the summary table is the difference between LocationE-LocationF= 0.712, so to get the right crown estimates for LocationF=LocationE-0.712, it would rather mean:
CR = 0.05*DBH - 0.71*Location + 1.78
If yes, why doesn't r simply shows a negative estimate?