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| instructor |
Prof. Yevgeny Goncharov |
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| contact
me |
202A Love Building;
645-2481 (office); 644-2202 (front desk) webpage: http://www.math.fsu.edu/~goncharo/ |
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| office hours | TR 12-1pm; or drop by if you feel lucky | ||
| prerequisite |
(1) MAS 3105 Applied
Linear Algebra and (2) either STA 4322
Mathematical Statistics or STA 5326 Distribution Theory and Inferences;
(3) students are not eligible for this course if they had ECO 5425 Time Series Analysis or STA 5856 Time Series and Forecasting Methods |
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| texts |
Kutner, Nachtsheim,
Neter, and Li, Applied Linear
Statistical Models, 5th Edition, McGraw-Hill,
2005. Brockwell and Davis, Introduction to Time Series and Forecasting, 2nd Edition, Springer, 2005. google: "Practical regression and anova using R" |
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| required software | R, an implementation of
S language, available at
http://www.r-project.org. No prior
knowledge of R
is expected. R
is available as Free Software and runs on a wide variety of UNIX
platforms and
similar systems (including FreeBSD and Linux), Windows and MacOS. To install on
Windows machines: under
"download" select CRAN >> under "Precompiled Binary
Distributions" select "Windows (95 and later) >> "select
"base"
>> download "rw2001.exe" to your harddrive and run it. |
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| objectives |
The
purpose of
this
course is to provide students with an introduction to
simple and multiple regression methods for analyzing relationships
among several
variables, and to elementary time series analysis. By the end of the
course the
student should be able to understand standard statistical
methods, and
use them
to fit linear regression and time series models to a variety of data. |
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| homework |
Weekly homework will be
assigned but not graded. They are essential for understanding... and
for satisfactory course grade as well. |
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| exams |
There will be two hour
tests on dates
to be announced, and a comprehensive final exam at the
University's designated final examination time: Tuesday, April 22,
5:30--7:30 noon. |
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| grading |
Your course grade will be a weighted average of two midterm grades (30% each), and final exam grade (40%). Your final letter grade will be assigned according to the usual scale: A 90%-100%, B 80-89%, C 70-79%, D 60-69%, F below 60%. Borderline grades will be resolved positively by good class participation and negatively by inconsistent attendance. | ||
| makeups |
No written makeups are
given. An unexcused missed exam
receives a penalty score. The grade on the final exam will be counted
for an
absence for a verifiable excused reason. |
| honor code: A copy of the University Academic Honor Code can be found in the current Student Handbook. You are bound by this in all of your academic work. It is based on the premise that each student has the responsibility to 1) uphold the highest standards of academic integrity in the student's own work, 2) refuse to tolerate violations of academic integrity in the University community, and 3) foster a sense of integrity and social responsibility on the part of the University community. |
| ada statement: Students with disabilities needing academic accommodations should: 1) register with and provide documentation to the Student Disability Resource Center (SDRC); 2) bring a letter to the instructor from SDRC indicating you need academic accommodations. This should be done within the first week of class. This and other class materials are available in alternative format upon request. |