At https://archive.ics.uci.edu/ml/datasets/Abalone, you will find a dataset of measurements by W. J.

At
https://archive.ics.uci.edu/ml/datasets/Abalone, you will find a dataset of
measurements by W. J. Nash, T. L. Sellers, S. R. Talbot, A. J. Cawthorn and W.
B. Ford, made in 1992. These are a variety of measurements of blacklip abalone
(Haliotis rubra; delicious by repute) of various ages and genders.

(a)
Build a linear regression predicting the age from the measurements, ignoring
gender. Plot the residual against the fitted values.

(b)
Build a linear regression predicting the age from the measurements, including
gender. There are three levels for gender; I’m not sure whether this has
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At
https://archive.ics.uci.edu/ml/datasets/Abalone, you will find a dataset of
measurements by W. J. Nash, T. L. Sellers, S. R. Talbot, A. J. Cawthorn and W.
B. Ford, made in 1992. These are a variety of measurements of blacklip abalone
(Haliotis rubra; delicious by repute) of various ages and genders.

(a)
Build a linear regression predicting the age from the measurements, ignoring
gender. Plot the residual against the fitted values.

(b)
Build a linear regression predicting the age from the measurements, including
gender. There are three levels for gender; I’m not sure whether this has to do
with abalone biology or difficulty in determining gender. You can represent
gender numerically by choosing one for one level, 0 for another, and -1 for the
third. Plot the residual against the fitted values.

(c)
Now build a linear regression predicting the log of age from the measurements,
ignoring gender. Plot the residual against the fitted values.

(d)
Now build a linear regression predicting the log age from the measurements,
including gender, represented as above. Plot the residual against the fitted
values. (e) It turns out that determining the age of an abalone is possible,
but difficult (you section the shell, and count rings). Use your plots to
explain which regression you would use to replace this procedure, and why. (f)
Can you improve these regressions by using a regularizer? Use glmnet to obtain
plots of the cross-validated prediction error.

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