Hypothesis Testing using Wald Criterion for two regression models with dummy variables

I have two models, controlled by dummy variables to see if the models can be
combined into one model with similar intercepts and slopes. Has anyone tried
to conduct this type of test in R. I am utilizing the econometric idea of
hypothesis testing through the hypothesis of coincidence. I have tried to
run an anova with test of Chisq, but I am not sure what the results are
telling. In addition, I used the rms package with a lrm model in an anova
test, again I am not sure what the results are telling me:

Try 1
anova(H,Ha,test="Chi")
Analysis of Variance Table

Model 1: logload ~ logflow
Model 2: logload ~ dummy + logflow
  Res.Df    RSS Df Sum of Sq  Pr(>Chi)    
1     17 4.6742                           
2     16 2.6314  1    2.0428 0.0004245 ***
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 

Try 2:
fit<-lrm(logload~logflow+dummy)

anova(fit)

Wald Statistics          Response: logload 

 Factor     Chi-Square d.f. P     
 logflow    17.56      1    <.0001
 dummy       5.22      1    0.0224
 TOTAL      18.03      2    0.0001

Can anyone help me with this?
Thanks!

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