Significant Difference between two linear regression models I'm trying to predict weight based of age and height for people under 30 and people 30+
I have the original data set mydata
I have then created two subsets mydatalessthan30 and mydata30plus
Then I have
A = lm(mydatalessthan30$weight ~ mydatalessthan30$age + mydatalessthan30$height)
B = lm(mydata30plus$weight ~ mydata30plus$age + mydata30plus$height)
I then want to check if there is a significant difference between the models, how should I do this?
I have tried Anova(A,B) but this doesn't work

I think anova(A, B) is not meaningful here since you are comparing models fitted to different datasets.
It seems to me that you are asking whether weight responds differently in people < 30 year-old vs people > 30 year-old while taking height into account. If so, you want to assess the effect of interaction between height and age, like:
fit <- lm(weight ~ age * height, data=mydata)
summary(fit)

where age is a dichotomous variable with two levels (<30, >30). That is, you are asking: how different is the slope of the regression line of height vs weight in <30 compared to the slope of height vs weight in >30?
As an aside, I wonder whether you actually need to dichotomize age since in doing so you reduce the information contained in the dataset.
