Hello everyone,
Recently, I faced a problem on explanatory of *Interaction variable* in
Linear Regression, could anyone give me some help on how to explain that?
the response variable Y is significantly correlated with *Interaction
variable X* which is consisted of Continuous predictor A and Categorical
predictor B. The Categorical predictor B has two factors B1 (value=1) and
B2 (value=0). The result is as follows:
Call:
lm(formula = Y ~ ... + *A:B*, data = ..., na.action = na.omit)
Residuals:
Min 1Q Median 3Q Max
-0.84267 -0.29877 0.01961 0.32187 0.98519
Coefficients:
Estimate Std. Error t value
Pr(>|t|)
(Intercept) 0.7699265 0.5129588 1.501 0.1408
BB1 -0.6657700 0.2668956 -2.494 0.0166 *
A 0.0017799 0.0007569 2.352 0.0235 *
... 0.2393929 0.2334615 1.025 0.3110
... -0.3877065 0.2317213 -1.673 0.1017
*BB1:A 0.0059008 0.0025522 2.312 0.0257 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4379 on 42 degrees of freedom
Multiple R-squared: 0.2813, Adjusted R-squared: 0.1958
F-statistic: 3.288 on 5 and 42 DF, p-value: 0.01354
*My questions:*
1. *How to explain the result of BB1:A correlated with Y? since BB1 is
only one factor of B, and if it is combined with A, how does the
combination mean?*
2. *Can I believe the significance of either single BB1 or A? Why?*
Thank you in advance for any possible help!
Chen,
a beginner in R and statistics
--
Chen Xiu
Guest Fellow/ PhD Student
Department of Conservation Biology
UFZ - Helmholtz Centre for Environmental Research
Permoserstr. 15
D-04318 Leipzig
Germany
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