# problem-3.30

problem-3.30 The slope appears in the output of lm(). The scatterplot shows a linear trend between the transformed variables that is not apparent in a scatterplot of the original variables.

    > library(MASS)                 # loads data set and lqs()
    > names(Animals)
    [1] "body"  "brain"
    > plot(log(brain) ~ log(body), data = Animals)
    > res = lm(log(brain) ~ log(body), data = Animals)
    > res

    Call:
    lm(formula = log(brain) ~ log(body), data = Animals)

    Coefficients:
    (Intercept)    log(body)
          2.555        0.496

To compare to the output of lqs().

    > lqs(log(brain) ~ log(body), data = Animals)
    Call:
    lqs.formula(formula = log(brain) ~ log(body), data = Animals)

    Coefficients:
    (Intercept)    log(body)
          1.816        0.776

    Scale estimates 0.464 0.463

There are three influential points in the scatterplot of the log-transformed data causing the big change in the slope of the regression line.