INSTRUCTOR: Prof. KARIN REINHOLD Office: ES 123A Phone: 442-4641 e-mail: [email protected] Class Time: MWF 1:40-2:35pm. Room: SLG12 Office Hours: MWH:11-12am |
TEXT: Intro Stats by De Veaux, Velleman \& Bock Pearson, Prentice Hall |
The grade in the course will be based on four exams of 100 pts each,
plus in class or on line quizzes
and/or projects (total of 100 pts).
Grade scale (in percentage of total grade): 100-94 = A, 93-87=A-, 86-81=B+,
80-75=B, 74-69=B-, 68-63=C+,62-57=C, 56-50=C-.
Familiarize yourself with Blackboard. The on-line quizzes and projects will be posted
there.
To pass the course you need to obtain a total average of 50\% or more, must
not obtain a grade of 30 or less in more than one exam or in the combination
of all quizzes.
It is your responsibility to be aware of the
dates of the exams and the content and due date of assignments.
If you miss a class, it is your responsibility to be aware of the topics
discussed during that class, the assigned homework and the
possibly given assignment. There are no make ups for in-class quizzes missed.
There is no reason to miss an exam
other than getting sick (bring note from doctor), being on a
team that has a game at the same time an exam is given (bring
a note from your coach), or a death or serious illness in your
family (bring a note from your family). In the event you can not attend an
exam, you must notify me in
advance, otherwise your grade for that exam will be 0.
You can contact me by phone (leave a message if I'm not in), stop by my
office (leave a note if I'm not in) or send me an e-mail.
Course description:
This course is part 2 of a basic course in statistics. Its pre-requisites
are: Math 108 or having taken statistics in highschool. You should be familiar with descriptive statistics,
histograms, boxplots, correlation, the basics of probability, the Binomial
random variable and the normal distribution.
We will continue hypothesis testing for proportions, estimating means with
confidence, testing hypothesis about means, Chi--square tests, simple and
multiple regression, analysis of variance, and lastly, non--parametric
methods. The class objectives are to learn (a) the mathematics behind the tests,
(b) being able to identify when to use a particular case, (c) interpret the
results.
To aid the computational aspects of the techniques and to do graphical
representations of data, we will be using Excel and R. You don't need to be
familiar with them, but we will learn what we need in both progrmas as we go.
Familiarity with excel will be a great tool to have for any future job. The
skills you'll learn with R can be translated to any other statistical
software like S-plus or SAS, widely used for applications of statistics - but
R is free.
EXAM SCHEDULE:
Exam 1: Sept 17
Exam 2: Oct 8
Exam 3: Nov 5
Exam 4: Dec 3