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Material Type: Exam; Class: Info Analy Mgrl Decisn; Subject: Decision Sciences; University: University of Oregon; Term: Unknown 1989;
Typology: Exams
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The following shows the first few rows of data:
num
usa s_a s_b s_c s_d s_e s_h s_m
s_o s_p s_r s_s s_t s_u s_w
s_x freq
last first web male
res
purch spend
part
1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3662 3662 1 0 1 1 128 s
2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2900 2900 1 1 0 0 0 s
3 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 2 3883 3914 0 0 0 1 127 t
4 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 829 829 0 1 0 0 0 s
5 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 869 869 0 0 0 0 0 t
6 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1995 2002 0 0 1 0 0 s
7 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1498 1529 0 0 1 0 0 s
Analysis
The following gives an overview of the analysis for this case which we will develop both in class
and for homework. Further details will be available as we work through it (i.e., do not follow
these directions explicitly).
a) Make a copy of the data sheet, sort by the "Purchase" variable, and remove the records
where Purchase = "0" (the resulting spreadsheet will contain only purchasers) – this
spreadsheet is called “Tayko.xls.”
b) Partition this data set into training and validation partitions on the basis of the partition
variable.
c) Develop models for predicting spending using
i) Multiple linear regression (use best subset selection) – homeworks 2 and 3.
ii) Regression trees – we will cover this in class during week 5.
d) Choose one model on the basis of its performance with the validation data.