25622: Quantitative Business Analysis

oatsandsugar —  August 6, 2011 — 2 Comments

This subject is somewhat reminiscent of High-School Maths.  If you did 3 unit maths or higher, the first half of the course is a cake-walk; otherwise, with a bit of practice, you can still do very well.  The second half of the course – econometrics – is quite simple, teaching you various statistical tools to analyse a set of data, working out things like “statistical significance” of variables.  Again, this is not too hard if you do your homework.

Henry Bruere (LOC)

Work this hard, you will do well!

The teachers

We had the most amazing lecturer, his name was Otto Konstandatos and he was a hilarious character that some-how managed to make integration and regression engaging.  Perhaps it was the humour, perhaps it was that he seemed to engage, but I really liked him.  Further, I had a wonderful tutor, Mr Patel, who was happy to answer any/all the many questions I had during the Semester.

The mid-semester exam

The mid-semester exam covered the content tought in the first half of the semester.  It was a wholly multiple choice exam, and it is a good opportunity to hoard some marks, since this part of the course is much less analytical than the second half.  With appropriate revision (doing the tutorial questions and a few practice exams and going to the UPASS/Maths Study Centre when you don’t understand something) it is quite achievable to get a HD, or even close to a perfect score in this exam.

You are allowed to bring in one A4 sized “cheat sheet” into the test.  To maximise its utility, I worked with 8 pages with a slightly larger font — my cheat sheet was designed to be printed 4 pages to a page, and double-sided.  My cheat sheet for the mid-semester exam covers is available here: 25622:MisdemFormulaSheet.

The exam was not that challenging, and just gave questions of a similar difficulty to the tutorial homework.  Further, time was not that much of an issue in this test.

The Assignment

The assignment is a group assignment.  As such, keep a look out in your tutorial for other people who seem to be smart, or have compatible personalities. Whilst I did the majority of the work for our assignment, I still thoroughly enjoyed working with my group because working, and laughing, with people is much less boring than working alone (that’s why I love study-groups!).

The first half of the assignment uses the mathematics we learned in the first half of the semester to prove the OLS (Ordinary Least Squares) Regression Model.  Basically, for the last few years, every single person who has done  this assignment has had to do this, so it is quite easy to find good answers.  You can find mine in the attached assignment.  This part of the assessment is worth half the marks, and, generally, is the place where people pick up most of their marks.

The second half of the assignment is applying the regression techniques we have learned over the semester to a data set, run through eViews, and answer analytical questions on the significance of various variables in the data.  In our assignment, we had to analyse the relationship between certain variables (including engine size, car weight, etc.) to the economy of vehicles.

Whilst I am happy to attach my assignment, I have to forewarn, our group got a distinction (16.5/20) and there were many groups who did better, so perhaps other sources are more appropriate.  Here is the assignment: QBA:Assingment.

The Final Exam

The final exam was broken into two halves, the first testing the content from the first half of the semester (which had already been tested in the mid-semester exam) and the second half was on econometrics (here are my econometrics formulae: 25622Econometrics).  Whilst the questions from the first half were easy (just a quick hint, it is almost always easier to work backwards on a Lagrange Multiplier question than it is to solve it from scratch), the problems on regression were somewhat unexpected.

In the mid-semester exam, the content of the exam reflected the content and difficulty of the textbook questions assigned as homework.  In the final exam, the questions were nothing like those in the textbook or the standard tutorial questions.  They were more theoretical.  The best preparation I had for them was doing questions at UPASS and at the Maths Study Centre.  Since it would be impossible to do a regression on paper (except for with the simplest data) within the time frame, a lot of the questions were about transforming the formulae of the OLS equations, theoretical questions on the assumptions of the model and/or questions analysing a pre-calculated regression output.  This exam, too, did not press me too much for time, although I did end up spending a lot more time on the second half of the exam.

Here is my final exam cheat-sheet: 25622FinalFormulae.  Again, print 4 pages to a page, double sided.

So, if you study consistently, or cram really well, you should pass the subject.  Attend all the lectures since that is the best way to learn — Otto has a marvellous way of making a topic (that the textbook makes seem incomprehensible) feel absolutely trivial.  Go to all the UPASS’s and Maths Study Centre sessions for the topics you don’t feel you understand.  If you do all that, you can really make this subject stand out on your transcript as a really good mark.  You can get a lot from putting a bit of time into this subject.

Appendix 1: Contents of midsemester exam cheat sheet

25622:MisdemFormulaSheet

  • Graphs, lines, polynomials, exponentials and logarithms
    • Transformation of a curve (vertical and horizontal) and stretching a curve
    • The quadratic formula (general, intercept and vertex form; transforming between forms of a quadratic equation)
    • Finding asymptotes (horizontal and vertical asymptotes)
    • The exponent (exponent laws)
    • The logarithm (log properties, log laws and change of base of log)
  • Financial arithmetic
    • Simple and compound interest; continuously compounding interest
    • Computing growth time
    • Annual percentage yield
    • Future value of an ordinary annuity; present value of an ordinary annuity
  • Derivatives

Appendix 2: Contents of Econometrics Summary Sheet

25622Econometrics

  • Descriptive statistics
    • Mean
    • Variance
    • Standard Deviation
    • Sample Covariance
    • Sample Correlation
  • Finding the Regression (OLS Model)
    • Regression formula with one regressor
    • Slope and intercept of regression formula
  • Finding R Squared (SSR, ESS and TSS)
  • Standard error (of the regression, and of “B Hat”)
  • Hypothesis testing (T and P values)
  • Multiple Variable Regression
    • Reparamatrisation
    • F-statistic tests
  • Non-linear regression models
    • Elasticity
    • Test for Heteroskedasticity

Appendix 3: Contents of Final Exam Formula Sheet

25622FinalFormulae

  • Graphs, Lines, Exponentials and Logarithms
    • Vertical and horizontal transformation
    • Stretching/squeezing
    • The Quadratic Function (finding the vertex form of a quadratic equation)
    • Finding asymptotes (vertical and horizontal)
    • Exponent Laws
    • Log Properties (and change of base of log)
  • Financial Arithmetic
    • Simple interest
    • Compound interest
    • Continuously compounding compound interest
    • Computing growth time
    • Annual Percentage Yield (APY)
    • Future and present value of an annuity (FV and PV)
  • Derivatives
    • Deriving from first principles
    • Basic differentiation properties
    • Derivatives of Logarithmic and Exponential functions
    • The product, quotient and chain rule
    • General derivative rules
    • Local extrema and graph sketching
    • Optimisation
  • Integrals
    • Indefinite integrals of basic functions
    • Integration by substitution (rules and method)
    • General integral rules
    • The definite integral (error bounds, properties)
    • The fundamental theorem of calculus
    • Average value of a continuous function
  • 3D+
    • Functions of several variables
    • Partial derivatives
    • Maxima, minima and Lagrange Multipliers
  • Descriptive statistics
    • Mean
    • Variance
    • Standard Deviation
    • Sample Covariance
    • Sample Correlation
  • Finding the Regression (OLS Model)
    • Regression formula with one regressor
    • Slope and intercept of regression formula
  • Finding R Squared (SSR, ESS and TSS)
  • Standard error (of the regression, and of “B Hat”)
  • OLS Wonder Equation; Adjusted R Squared
  • Hypothesis testing (T and P values)
  • Multiple Variable Regression
    • Reparamatrisation
    • F-statistic tests
  • Non-linear regression models
    • Elasticity
    • Test for Heteroskedasticity
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oatsandsugar

Posts

LLM candidate at Cornell Tech. Consultant for King & Wood Mallesons and Project Evangelist for Legalese.

2 responses to 25622: Quantitative Business Analysis

  1. 

    Hi Yokhi,
    Just wondering if your doing any Law Subjects this semester.
    See you around champ!

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