title: slope for linear models with different intercepts: how to standardize/normalize slopes to compare?
body: <p>I have code to fit linear models to data in R. My independent variable is AGE and my dependent variable is Costs. I am interest whether Costs in general increase with AGE in years. However for some parts my intercept is 10 whereas for others my intercept is 1000 and thus an increase in currency units is not helpful, since a slope of 1 unit per year might be a lot for an intercept of 10 and a slope of 1 currency unit per year is neglectable. Could anybody help with how to solve this issue, to standardize slopes in R to compare them after calculating them with <code>lm</code>?</p>
  
  <p><strong>Example</strong></p>
  
  <pre><code>data.ex &lt;- data.frame(Age = c(c(1:10), c(1:10)),
                    Costs = c(11,12,13,14,15,12,17,18,19,20, 1001,1002,1003,1004,999,1006,1007,1008,1009,1010),
                    Type = c(rep("A", 10), rep("B", 10)))
  pt &lt;- ggplot(data = data.ex, aes(x=Age, y = Costs))+
  geom_smooth(method="lm")+
  facet_wrap(facets = "Type", nrow = 2)
  plot(pt)
  print(with(data.ex[data.ex$Type == "A", ], lm(Costs ~ Age)))
  print(with(data.ex[data.ex$Type == "B", ], lm(Costs ~ Age)))   
  </code></pre>