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  UST 803  Course Syllabus Syllabus

(any changes will be announced in class)


Objectives COURSE OBJECTIVES


Quantitative Research Methods I is the first of a two-course methods sequence designed to provide Ph. D. students in Urban Studies with tools and skills necessary in quantitative/qualitative research. The first course in the sequence focuses on linear regression techniques. A good understanding will enable students to apply these techniques, as well as acquire on their own additional multivariate statistical techniques rooted in linear methodology, such as discriminant analysis, factor analysis, or structural equations. This course presents single-equation regression models with two and three variables, including estimation and inference. It also examines how regression is used and interpreted when data do not conform to some of its basic assumptions, such as normality or homoscedasticity of errors. The course identifies: the nature of the deviations from assumptions; the resulting estimation and interpretation problems; practical consequences; detection; and, some remedies.
At the conclusion of the first course, students will:
    understand the nature of the basic linear regression model and its statistical underpinnings and basic assumptions;
    formulate research questions that require the use of linear regression;
    set up, solve and evaluate a regression model;
    interpret results obtained using a statistical package;
    critique results reported in professional journals by other researchers;
A major component of this course is the reading and presentation of articles in the students' areas of interest. Each student will select and present two articles using the methodologies studied.


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Method COURSE METHOD


The course consists of:
  • lectures on statistical techniques and procedures;
  • class discussions and student presentations of published articles;
  • discussions of homework solutions.
  • student presentations of professional articles in their field of interest.

Important!


  • Students are expected to: attend all classes; participate actively in discussions, asking clarifying questions; use the computer (as frequently as possible) for solving homework sets and (always) for text editing; make presentations.
  • Homework sets and project outputs should be handed in ON TIME and TYPED. Since communication skills complement the analytic ones, pay attention to completeness, clarity and aspect. Grading is based on sound analysis, effective interpretation, and communication of results.
  • Prepare for class sessions by reading text assignments and identifying topics that need clarification in class. Feel free to raise questions to ensure thorough understanding and ability to apply procedures in contexts outside the classroom.
  • Class presentations: Each student will present the following during the Semester:

    two journal articles from his/her field of interest, featuring regression methods/problems
    solutions to homework problems
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Prerequisites PREREQUISITES

  • College (scalar) algebra (ability to handle it);
  • Elementary statistics (B or better in recent UST 601 or equivalent, subject to advisor approval);
  • Ability to use some statistical package that accommodates regression (e. g., SPSS, MathCAD).



Evaluation EVALUATION PROCEDURE (tentative)

The final grade will be a composite of grades for:
  • periodic assignments (expected every week), 10%
  • class presentations, 15%
  • midterm 1 (usually 5th week, class time), 20%
  • midterm 2 (usually 10th week, class time), 25 %
  • final (exam week, during exam week at class time), 30%


Late homeworks will not be accepted since solutions are discussed in class.
The (open book) midterms and final will test accumulated knowledge as well as ability to respond to new problems. While focusing on the most recent lecture topics, exams have to rely on concepts covered earlier; in preparation, review earlier material and avoid falling behind in readings or homeworks.
Attendance at all exams is required. Makeups will be given only in emergency cases (proof required; vacation arrangements are not emergencies) and with advance notice.
If any course component is not offered, the points are redistributed among remaining components.


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Computer Lab COMPUTER WORK


For solving problem sets, students should feel free to use any computer software with which they are familiar, such as SPSS, SAS, etc. An SPSS workshop is offered at LCUA at the beginning of each semester (and recommended).

Part of this course is a computer lab where students learn to work with the software MathCad, useful for simulating and visualizing data sets. Work should be saved on a diskette. At the end of the project each student will hand in the diskette with all the programs, as well as a printout of the results. The following lab protocols are available in .pdf format (for printing).
  • LAB#1: Tutorial and Two-Variable Regression
  • LAB#2: Monte Carlo Simulation of the Two-Variable Regression
  • LAB#3: Mean Prediction, Confidence Interval
  • LAB#4: Cobb-Douglas Production Function

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