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1 Introduction
1.1 Syllabus
1.1.1 Level of the course:
The course is given at an intermediate level. The course requires one year of calculus and
a certain degree of mathematical maturity. This is an ambitious course in that we cover
both probability and statistics in one semester. Because so much material is covered, it
is impossible to go over a large enough number of examples that illustrate the subject
as it is being developed. Therefore, students should expect to spend at least ﬁve hours
a week reading the book, reading the references, and going over the book examples, the
recommended problems, and the assigned problems. It is very important not to fall behind
because the material builds up very quickly. On the positive side, the reward is that after
one semester you will have a working knowledge of probability and statistics. The course
requires students to use the textbook for examples and details which can’t be covered in
class because of time limitations and coverage requirements. To alleviate this problem I will
be holding voluntary attendance recitation sessions where the TA will go over questions and
exercises.
1.1.2 Encourage students to ask questions
1.1.3 Use of statistical computer packages
Throughout the course, students will need to crunch data. Students can use the statistical
package of their choice, e.g. Minitab, SPSS, S, etc.
. The statistical modules embedded in
Excel for Windows is powerful enough to work most of the problems in the textbook.
1.1.4 Motivation
Why study probability and statistics? One answer, of course, for many of you is that this
is a required course. But why? The reason is that we live in a world where uncertainty is
everywhere. Will it rain tomorrow? Which candidate will win the elections? Is treatment A
better than treatment B? Is production out of control? Should we target generation Y instead
of generation X? While we cannot give a deﬁnitive answer to most of these questions, we
can observe the underlying process, collect data and give an answer couched in probabilistic
terms. Thus, we may say that there is an eighty percent chance that it will rain tomorrow,
and we may reject the claim that treatment A is better than treatment B and at the same
time announce the probability that we are wrong. In general, in statistical inferences we
collect data and want to make intelligent and rigorous statements about a population from
1
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View Full Documentwhich the data comes from. Examples include polling, quality control, medical treatments,
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 Spring '04
 GALLEGO

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