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Scenario: I have data comparing the number of tree stems in 30 forest plots between two sampling years (1992 and 2012). Each plot received hurricane damage between these 2 sampling years -- this damage was coded as being 0-100% of trees felled/damaged.

I ran a linear regression using lm() in R including a centered year term, hurricane damage, and an interaction term between them.

I get the following output:

Call:
lm(formula = Count.Ha ~ I(Year - 1992) * HurrDam, data = dataset, ])

Residuals:
    Min      1Q  Median      3Q     Max 
-368.84  -69.79  -23.01   81.30  413.28 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)            147.3300    50.7297   2.904  0.00529 ** 
I(Year - 1992)         -17.2595     3.4007  -5.075 4.73e-06 ***
HurrDam                 -1.4680     1.6764  -0.876  0.38503    
I(Year - 1992):HurrDam   0.7634     0.1128   6.766 9.11e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 138.1 on 55 degrees of freedom
Multiple R-squared:  0.5886,    Adjusted R-squared:  0.5662 
F-statistic: 26.23 on 3 and 55 DF,  p-value: 1.15e-10

As you can see, Year is significant as is the interaction term, but HurrDam is not. How do I interpret this??

  • I've seen a number of posts discussing intepretation when discreet variables or even continuous non-bounded variables are involved, but I'm not sure how my inclusion of a time variable and a bounded percentage as a variable impact the way one would interpret these results.

  • Note: my ultimate hypothesis I'm trying to investigate is that the number of stems did not increase with time except in plots with greatest hurricane damage.

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