STA103
Probability/Statistical Inference
Jenise’s contact info
Instructor: Jenise Swall
 Office: 221 Old Chem Bldg.
 Phone: 684-4608
 Office hours: Wed. 9:30PM-10:30PM,
Thu. 1:30PM-2:30PM
 jenise@stat.duke.edu

TA contact info
Christine KohnenMickelson
 Office: 212 Old
Chem Bldg.
 Phone: 684-4365
 Office hours: TBA
 cnk@stat.duke.edu
Tao Jiang (Tom)
 Office hours: TBA
 tao.jiang@duke.edu
Overview
Covers skills needed for further study
in econ or quantitative social science
 Much more mathematically-intensive
than STA101 or STA102
 You must be familiar with calculus at
least at the level of MTH31
 Practice is essential – expect to put in
many extra hours outside of class

Topics

First part of course: probability concepts
– Basics, conditional probability, Bayes’ theorem
– Discrete and continuous random variables
– Joint distributions

Second part of course: statistical concepts
–
–
–
–
Estimators, sampling distributions, bias
Confidence intervals, hypothesis testing
Maximum likelihood estimation
Regression
CourseInfo page
Course web page is primary reference
point for schedules, assignments, etc.
 CourseInfo system used to maintain
the site and provide security so you
can view your grades online
 You must be enrolled in STA103 to
make full use of the CourseInfo page

Using CourseInfo
First, obtain your CourseInfo userid
and password. Detailed instructions
are on the “public” STA103 page:
www.stat.duke.edu/courses/Spring01/s
ta103
 Relevant info will be on both pages
until add/drop ends, for the
convenience of those waitlisted.

Course materials
Required text: Mathematical Statistics
with Applications by Wackerly,
Mendenhall, Scheaffer (5th edition)
 Optional text: Student solutions
manual for the textbook
 Calculator capable of logs,
exponentiation, powers, etc. for
quizzes/exams

Sections
Supervised by TAs
 Quizzes administered each week
 Some computer exercises will be
incorporated, mostly in the last portion
of the course (using S-Plus software)
 If you need to switch sections, please
see me or send me an email

Quizzes
Quizzes administered weekly, but may
be cumulative in nature
 Students must take quizzes in their
assigned sections
 Lowest quiz grade will be dropped
(can be used as one unexcused
absence)

Exams
Final scheduled by the Registrar for
04MAY on 9AM-12N
 Two midterms during regular class
hours (tentative dates):

– Midterm 1: 13FEB
– Midterm 2: 29MAR
Quiz/exam regrades
You have 2 weeks after test/quiz date
to request a regrade
 Submit a note detailing the nature of
the grading error along with the
quiz/exam to your TA
 Papers submitted for regrade may be
examined in their entirety; either net
gain or net loss possible

Homework
Suggested problems (and solutions)
will be posted on the web site as we
go along
 Intended to help you gauge your
progress and review the material
 Will not be graded

Absences
Dean’s excuse must be presented to
be excused from quizzes or to
reschedule exams
 Athletic team schedules, illness, or
other less official excuses will not be
accepted in place of a Dean’s excuse

Descriptive statistics
Statistics that we usually see in the
media and other everyday events are
descriptive statistics
 These are just summaries of data
 They include charts, graphs, summary
statistics (mean, standard deviation,
etc.), and other such displays

Probability & sampling



Because the population is large, the sample
is a useful way of understanding it
If we know what the make-up of the
population is, then we can calculate the
probability of obtaining a certain sample
Caution must be exercised when choosing a
sampling scheme to avoid bias
Inference



Inferences are the conclusions made about
the population after considering the sample
Inferences usually concern quantifiable facts
that we are interested in about the
population (mean, variance, etc.)
Since inferences are rarely exactly correct,
we also want to estimate how close we can
expect ours to be
Probability/inference
Use inference
Sample
Population
Use probability
Simple example



Question of interest: In At Duke, what
percentage of students are econ majors?
Method 1: Ask each undergraduate about
his/her major, tally results, and find the
answer
Method 2: Ask a sample about their majors,
tally results, and make an estimate
All undergraduate students
Psych Econ Econ
Econ Econ Econ
Econ
Econ
Econ
Econ Psych Econ
Bio
Soc Econ
Econ EconCS
Econ
Econ Soc
Soc Soc
CS
Bio
Econ
Small sample of undergrads
Simple example (cont.)
It is unlikely to get a sample like the
one obtained in the previous diagram,
but we have to recognize that it’s
possible
 Probability and inference concepts
work together to help us determine
whether to rely on our estimates

Graphical summaries
Command of descriptive stats (both
graphical and numerical) needed for
further study
 Most common graphical summary for
us will be the histogram
 Histogram bars have area in
proportion to the number of data points
that fall in the interval they cover

Histogram intervals
Column 1

0.15
0.10
0.05
60
70
Column 1

0.30
0.20
0.10
60
70
Note that choice of
interval affects the
shape of the plot
and audience’s
perception
Care should be
taken to choose
intervals in an
appropriate way
Numerical descriptions

Measures of central tendency
– “Where’s the middle of the data?”
– Two major ones are mean and median

Measures of dispersion
– “How much variation is there in the
data?”
– Major ones are variance, standard
deviation, and inter-quartile range (IQR)