This is roughly saying that the mean response in the brain is a multiple, ($e^2.15$), of the body weight raised to $0.752$ and the errors are multiplicative, not additive. (Thanks to Demetrios Papanastassiou.)
fm <- weight/2.2 ~ I(height*2.54/100^2)
is missing a parentheses so that the squaring is of the entire expression:fm <- weight/2.2 ~ I( (height*2.54/100)^2 )
This will change the regression values to
Call:
lm(formula = fm, data = kid.weights, subset = idx)
Coefficients:
(Intercept) I((height * 2.54/100)^2)
15.571 4.239
The slope of 4 is more reasonable. (Thanks to Demetrios Papanastassiou.)
p125: The formulas on robust regression should read $\sum(y_i + (b_0+b-1x_i))^2$ and $\sum \rho(y_i + (b_0+b-1x_i))$. (Thanks to Demetrios Papanastassiou.)
p214, 215: The example on sample
uses k
to refer to the data vector, but ks
in the sample code. These should be the same. IN addition, the labeling of the "spinner" does not match the labeling of the spike plot illustrating the distribution. The values should be shifted by 1.
p218 line -1: The are to the left of $b$ is being referred to, not to the right of $b$.
p222: The comments explaining dunif
and punif
are switched. The explanation that dunif(x=1, min=0, max=3)
is 0.3333
is because $1/(b-a)$ takes that value. The explanation for punif(q=2, min=0, max=3)
is 0.6667
is because $2/3$rds of the area is to the left of $2$.
p229: figure 6.6. The illustration was to show that the area to the left of one standard deviation from the mean is the same, but actually we see 1.5 standard deviations, not 1.
p231 line -9: spurious %
symbol following 50.
p262 line 9: an error of 55.9 should be $0.0559$, not $0.059$.
p267 line 4: Should be "What is a 90% condidence interval" and not "confidence level."
p270, line -3: Divisor should be M
and not n
. This would give a value of $0.9$.
p273 Question 8.6 Making Coffee. The question asks for an 90% confidence interval, but the answer calculates using 80%.
p280, line -2: the data set is nym.2002
not nyc.2002
.
p 308, line -16: the argument to qnorm
is named mean
, not mu
.
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