 Marks: you can find the marks yourself. Just follow the steps below.
 Install MATLAB, which is freely available for McGill students; if you have MATLAB already, ignore this step
 Download the two files grades.p and grades.m (UPDATED: January 04, 2017; please redownload if your version is outdated) provided here
 UPDATE 20161012: initial version with marks for Assignment 1
 UPDATE 20161028: redesigned version with marks for Midterm added
 UPDATE 20161225: with marks for Assignments 2 and 3 and Final added
 UPDATE 20170104: with Q6 removed from raw score and with normalising denominator fixed at 70
 In MATLAB, set MATLAB directory to the two downloaded files
 Type help grades in MATLAB to learn how to use the files;
 Example: type grades(123456789) to see marks for student with ID 123456789
 Remark: before an exam has taken place, everyone gets 0 for that exam; that will of course be fixed once the corresponding exam has happened and has been graded
 Assignment files
 Midterm
 Grading progress: complete; see grading notes
 Descriptive statistics: max = 97.22%, mean = 42.08%, std = 27.82%
 Final
 Grading progress: complete!
 How the final is marked: each student receives a raw mark based on the student's performance on the exam. This raw mark is a nonnegative number between 0 and 96. The student's effective final score is then the ratio between the raw mark (studentspecific) and a normalising denominator (common to all students).
 Normalising denominator: 70
 Descriptive statistics for the normalised scores (among students who actually wrote the exam): max possible = 100%, max actual = 100%, avg = 62.39%, std = 20.56%
 Course grade:
 Let F be the normalized final score
 Let M be the midterm score
 Let A be the average among 3 assignments
 The course grade G is computed as: max{F, (F+M)/2, (F+(M+A)/2)/2}
 Note: F, M, A, and G are all measured in percents
 Normalized final v.s. midterm: (this only includes students who actually wrote the final) (so this is a plot between F v.s. M, NOT G v.s. M)
 Notes
McGill, Winter 2016, Econ 665 (Quantitative Methods) Instructor: Vinh Nguyen
McGill, Fall 2015, Econ 468 (Honours Econometrics I)  Syllabus
 MATLab files (built on MATLab 2015b x32):
 Assignment 1 files (link removed as course is over!)
 Assignment 2 files (link removed as course is over!)
 Assignment 3 files (link removed as course is over!)
 Assignment 4 files (link removed as course is over!)
 Assignment 5 files (link removed as course is over!)
 Notes from OH:
 20151112
 20151119 (an intro to tests of hypotheses)
 20151126 (book exercises 4.2 and 6.7)
 20151203 (notes on projection matrices and book exercises 2.16 and 2.19)
 20151210:
 Part 1: solutions and hints for Exercises 4.4, 4.6, 4.8, 4.9 (removed as course is over!)
 Part 2: (unedited!) notes from the tutorial (a data question on seasonality effects and a question about bootstrap tests in the context of IV regressions)
 Other notes:
 Nonparametric bootstrap through an example: rescaling residuals and other issues
 Due to time constraints, a planned review on GNR is cancelled. Sorry!
 About the grades:
 The final is out of 154. Your relative score for the final is computed with this formula: min{(raw score / 100),1}. In short, you are scaled by 100 (instead of 154) and if your relative score exceeds 1, it is rounded down to 1. The effective denominator 100 is chosen by Professor Davidson and the raw scores are supplied by me.
 Histogram for relative scores on the final exam:
Histogram for the overall performances in the course (among those who have written the final exam):
Regressing the relative score on the final exam and the final grade on a constant, homework average, and midterm score yield:
 Final exam   Final grade    Coefficient  pval  Coefficient  pval  Constant  0.2025  0.0336  0.1976  0.0034  Homework  0.2421  0.0438  0.2371  0.0052  Midterm  0.4423  0.0052  0.5339  0.0000 

