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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