** Text: **
Stats: Data and Models

**Grade**: Your
grade will be determined via
a Big Assignment (10% of your grade),
Exam 1 (20% of your grade each),
Exam 2 (30% of your grade), and the
Final Exam (40% of your grade).
Three quarters of each exam's contribution to your grade will consist of
modified homework problems. The homework assignments and exam dates
can be found in the class log.

**Honor Principle**:
On the exams, no help is to be given or received.

**Students with disabilities**: I encourage students with
disabilities, including "invisible" disabilities like chronic diseases and
learning disabilities, to discuss with me any
appropriate accommodations that
might be helpful.

**X-Session**: The X-session will be used twice for review sessions
(April 13th and May 11th). The X-session may also be used to if we
get behind in the syllabus, we are forced to cancel lectures, or for
help sessions where I can help orient you to various computational tools (like R).

**Rough Syllabus**
:
An introduction to the basic concepts of statistics. Topics include elementary probability theory, descriptive statistics, the binomial and normal
distributions, confidence intervals, basic concepts of tests of hypotheses, chi-square tests, non-parametric tests, normal theory t-tests, correlation, simple
regression, analysis of variance, and (if time permits) multiple regression and multi-factor analysis of variance. We hope to cover
every topic in our book, and hence our book's table of contents can be used as a detailed course syllabus. Also, the
R-environment
for statistical computing and graphics will be used. We will be learning how to use R, and, in particular, we will see how to perform and interpret most
of the tasks
in the following
R-Tutorial .