Mathematics 258 Categorical Data Analysis Fall 2012 1. Course ...
Mathematics 258 Categorical Data Analysis Fall 2012 1. Course ...
Mathematics 258 Categorical Data Analysis Fall 2012 1. Course ...
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<strong>Mathematics</strong> <strong>258</strong> <strong>Categorical</strong> <strong>Data</strong> <strong>Analysis</strong> <strong>Fall</strong> <strong>2012</strong><br />
<strong>1.</strong> <strong>Course</strong> Information<br />
Department<br />
Time<br />
Location Sweeney Hall 344<br />
<strong>Course</strong> Website<br />
Instructor<br />
Office Hours<br />
2. <strong>Course</strong> Description<br />
<strong>Mathematics</strong>, San Jose State University<br />
Mondays & Wednesdays, 5:30–6:45 PM<br />
<strong>Course</strong> materials, such as problems sets and solutions, are accessible through<br />
Desire2Learn (https://sjsu.desire2learn.com/).<br />
Dr. Bee Leng Lee<br />
Office : MH414<br />
E-mail : lee@math.sjsu.edu<br />
Phone: 924-5099<br />
Mondays & Wednesdays 2:50–3:35 PM, 4:30–5:15 PM.<br />
Note: If you wish to ask questions on the homework problems during the office<br />
hours, you are expected to have completed the relevant reading assignments,<br />
read the problems thoroughly, and at least attempted to solve them.<br />
Prerequisite<br />
Description<br />
Objectives<br />
Textbook<br />
References<br />
MATH 161A or MATH 261A. MATH 261A may be taken concurrently.<br />
This course introduces data analysis techniques for categorical data.<br />
Upon successful completion of this course, students should be able to recognize<br />
different types of categorical data and use appropriate statistical methods<br />
for them. They should also be able to conduct statistical analysis using existing<br />
software and properly interpret the output.<br />
Agresti, A. (2002). <strong>Categorical</strong> <strong>Data</strong> <strong>Analysis</strong>, second edition, New York: Wiley.<br />
Agresti, A. (2007). An Introduction to <strong>Categorical</strong> <strong>Data</strong> <strong>Analysis</strong>, second edition,<br />
New York: Wiley.<br />
Simonoff, J. S. (2010). Analyzing <strong>Categorical</strong> <strong>Data</strong>, New York: Springer.<br />
Thompson, L. A. (2009). R (and S-PLUS) Manual to Accompany Agresti’s<br />
<strong>Categorical</strong> <strong>Data</strong> <strong>Analysis</strong> (2002) 2 nd edition<br />
https://home.comcast.net/˜lthompson221/Splusdiscrete2.pdf<br />
3. <strong>Course</strong> Requirements<br />
Homework<br />
Exams<br />
will be assigned on a regular basis. Late work will not be accepted.<br />
will include materials from lectures, reading assignments, and homework. No<br />
early or late exams will be given. If you miss an exam, you will receive an F<br />
grade for the course. You will need a scientific calculator for the exams. PDAs<br />
and cell-phones are not allowed. Final exam is scheduled for:<br />
Monday, December 17, 5:15–7:30 PM.<br />
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4. Grading Information<br />
Final grades for this course will be determined using the following weights:<br />
Homework : 50%<br />
Exams : 50%<br />
5. Topics<br />
Distributions and inference for categorical data, describing contingency tables, inference for contingency<br />
tables, introduction to generalized linear models, logistic regression, building and applying<br />
logistic regression models, logit models for multinomial responses, loglinear models for contingency<br />
tables, models for matched pairs.<br />
6. University, College, or Department Policy Information<br />
See http://www.sjsu.edu/math/courses/greensheet/<br />
THE CONTENT OF THIS GREENSHEET IS SUBJECT TO CHANGE AND ANY CHANGES WILL BE ANNOUNCED<br />
IN CLASS. IF YOU MISS A CLASS, IT IS YOUR RESPONSIBILITY TO FIND OUT FROM YOUR CLASSMATES<br />
WHAT YOU HAVE MISSED.<br />
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