|BUS 41100 is a course about regression, a powerful and widely used data analysis technique.
Students will learn how to use regression to analyze a variety of complex real world problems.
Numerous empirical examples from nance, marketing, economics, politics, sports, etc are
used to illustrate applications of the material covered. Emphasis will be placed on analysis of
actual data-sets. Topics covered include: (i) simple linear regression; (ii) multiple regression;
(iii) linear prediction; (iv) residual diagnostics; (v) time series (autocorrelation functions,
auto-regression) (vi) logistic regression. The class will use both R and Excel as software.
You can use other software if you like (Minitab, Jump, Matlab ...).|
|There is no course pack or required textbook for the class. All lecture notes and course
materials will be available on the web. Some students find having a supplemental text useful
for studying. I recommend the textbook Applied Regression Analysis, by Terry Dielman for those that want additional reading but I won't be looking at it.|
|Grades will be determined by short quizzes (5%), a midterm exam given on the 6th week
(45%), and a take-home final exam (50%). The midterm exam will be closed-book/closed-
notes. A "cheat sheet" with formulas will be provided. The take-home final exam/project
is individual and will be due on Thursday/Friday of final exams week. There will be optional (practice) homework assignments throughout the quarter. Solutions for the homework
problems will be provided.|
|The prerequisite for this course is BUS 41000 or its equivalent including the following con-
cepts: Random variables, normal and t distributions, linear combination of random variables, hypothesis testing, confidence intervals and sampling distribution of the mean. I will present a quick review of the listed items whenever necessary. As an alternative, Chapter 2 of Dielman gives a good review of the necessary material.|
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