ECON20003

Quantitative Methods 2

Subject Outline

Introduction

Welcome to ECON20003, Quantitative Methods 2 (QM2). Having solid quantitative problem-solving skills is essential for future careers in commerce. Learning these skills can be challenging, but they are becoming crucial for success in business and economics. QM2 should not be a hard subject, but it does require the mastery of analytic skills, and thus it necessitates your constant attention throughout the semester.

Subject Overview and Aims

The overall aim of this subject is to help you become proficient in the use of quantitative techniques essential for analysis in business and economics. A wide range of skills will be covered during the semester. On successful completion of the subject, you should be able to:

(a) Identify the correct technique to solve a particular quantitative problem,

(b) Implement each technique, and

(c) Interpret the results from these techniques.

You will use the skills you develop in QM2 in the business, economics, finance, marketing, and management subjects you study during the remainder of your time at the University of Melbourne, and, most importantly, later in the workplace.

Learning Outcomes

Learning Outcomes and Generic Skills

To view the subject objectives and the generic skills you will develop through successful completion of this subject, please see the University Handbook: https://handbook.unimelb.edu.au/2021/subjects/econ20003

Awareness Issues

At a broader level, studying this subject will increase your awareness and the breadth of questions that are investigated within business and economics, the wide range of statistical information that is publicly available, and the future subjects you can take to learn more quantitative techniques.

Eligibility and Requirements

To view the eligibility and requirements, including prerequisites, corequisites, recommended background knowledge and core participation requirements for this subject, please see the University Handbook: https://handbook.unimelb.edu.au/subjects/econ20003/eligibility-and-requirements

Academic Staff Contact Details

Please see the subject LMS site for full contact details of the teaching staff in this subject.

Subject Coordinator and Lecturer Contact Details

Name: Dr László Kónya

Email: [email protected]

Phone: 8344 0355

Consultation Hours: On Zoom on Tuesday 11:00 – 12:00.

Email Protocol

Please note that we are only able to respond to student emails coming from a University email address. Please do not use personal email addresses such as Yahoo, Hotmail or even business email addresses. Emails from non University email addresses may be filtered by the University’s spam filter, which means that we may not receive your email. All correspondence relating to this subject will only be sent to your University email address. Note that you must first activate your University email address before you can send or receive emails at that address. You can activate your email account at this link: http://accounts.unimelb.edu.au/.

While academic staff endeavour to address queries received via email, it is more appropriate to resolve substantive questions during lectures and tutorials and/or during normal consultation hours. With this in mind, we encourage students to attend all lectures and tutorials and to familiarise themselves with the consultation hours offered by the lecturers and tutors in this subject.

Lectures

Lecture Times and Venues

ECON20003 is a dual delivery subject.

There are two one-hour lectures a week and two equivalent lecture streams.

(1) Monday 14:15 – 15:15 (FBE-G06 - Prest Theatre) and Wednesday 13:00 – 14:00 (The Spot-B01 - Copland Theatre)

(2) Tuesday 9:00 – 10:00 (Medical-C216 - Sunderland Theatre) and Thursday 9:00 – 10:00 (FBE-G06 - Prest Theatre)

Lecture Schedule

Week	Lecture (Date)	Topic	Required Reading from   W 2nd ed. & S 8th ed.
1	1 (26 & 27 July)   2 (28 & 29 July)	Introduction and General   Information about Quantitative   Methods 2 (QM2)   Estimation and Hypothesis Testing	W: Ch 1-2, § 3.1-3.6, 3.8-3.9   S: § 8.3, 9.3-9.4, 10.1-10.3, 10.5, 12.1-12.4
2	3 (2 & 3 August)   4 (4 & 5 August)	Desirable Properties of Point   Estimators   Parametric and Nonparametric   Techniques   The Assumption of Normality	W: 3.7   S: § 10.1, Ch 20 Introduction
3	5 (9 & 10 August)   6 (11 & 12 August)	Comparing Two Population Means   or Central Locations with   Parametric and Nonparametric   Techniques	S: § 11.1-11.3, 13.1-13.2, 20.1-20.2
4	7 (16 & 17 August)   8 (18 & 19 August)	The Chi-Square, t and F   Distributions   Inferences about One or Two   Population Variances   Inferences about One or Two   Population Proportions	W: § 3.5   S: § 9.5, 10.4, 11.4, 12.6, 13.3, 14.1-14.2
Assignment 1 due in by 10am on Monday 23 August
5	9 (23 & 24 August)   10 (25 & 26 August)	Comparing Several Population   Means with One-Way Analysis of   Variance (ANOVA) Based on   Independent Samples and   Randomised Blocks	S: § 15.1, 15.3-15.4, 20.3
6	11 (30 & 31 August)	Chi-Square Tests for the Analysis   of Frequencies   Measures of Association	W: § 3.4   S: § 5.4, 16.1-16.2, 16.4, 17.6
	Mid-semester   online test from   10am 31 August till   3pm 2 September.
	12 (1 & 2 September)
7	13 (6 & 7 September)   14 (8 & 9 September)	Linear Regression: Specification,   Estimation and Assessment	W: Ch 4-5, 6.1-6.3   S: § 17.1-17.4, 17.7, 18.1-18.2
8	15 (13 & 14 September)   16 (15 & 16 September)	General F-test   Omitted and Irrelevant Variables   Alternative Functional Forms   Multicollinearity	W: § 6.6, 6.8, 7.1-7.3   S: § 17.5, 17.7, 18.1-18.2, 19.1
Mid-semester break from Monday 20 September to Sunday 26 September
Assignment 2 due in by 10am on Monday 27 September
9	17 (27 & 28 September)   18 (29 & 30 September)	Heteroskedasticity   Using the Sample Regression   Equation   Dummy Independent Variables in   Regression Models	W: § 5.3, 6.7, 8.1-8.2, 9.1-9.3   S: § 17.5, 17.7, 18.3, 19.2-19.3
10	19 (4 & 5 October)   20 (6 & 7 October)	Dummy Dependent Variable   Regression Models: Linear   Probability, Logit and Probit   Models	W: § 8.3-8.5, 8.7   S: App. 19.A
11	21 (11 & 12 October)   22 (13 & 14 October)	Cross-Sectional vs. Time-Series   Data   Regression Analysis with Time   Series Data   Autocorrelation	W: § 10.1-10.3, 11.1-11.2, 11.4   S: § 4.2, 18.4
Assignment 3 due in by 10am on Monday 18 October
12	23 (18 & 19 October)   24 (20 & 21 October)	Stationary and Non-Stationary   Processes   Spurious Regression   Dickey-Fuller Unit Root Tests	W: § 12.1-12.3

Lecture Slides

Lecture slides will be made available on LMS prior to each lecture. Students are encouraged to read the slides and the relevant parts in the prescribed and/or the recommended textbook before attending or watching each lecture. Be prepared to take some notes in lectures, as some important explanations of the material might not be detailed on the slides.

Recorded Lectures

Every teaching week of the semester the two one-hour lectures of the first lecture stream will be recorded and made available on LMS on Monday after 4:00 pm and on Wednesday after 3:00 pm. You can access the recorded lectures by clicking on the Lecture Recordings (or similar) menu item on the LMS page for this subject.

Please review the Lecture Capture Student Guides page for more information: https://lms.unimelb.edu.au/support/guides/students/lecture-capture-student-guide.

Tutorials

Tutorials commence in the first week of semester (week beginning Monday 26 July).

Tutorial dates, times and locations can be found on the University timetable.

Enrolling in Tutorials

Students should enrol in tutorials via the Student Portal. After subject registration, students are allocated to available classes. It is the students’ responsibility to ensure their registrations produce a clash-free timetable. A change to your allocated tutorial time can only be made if there is space in alternative tutorials. The tutors and the lecturer cannot help students with tutorial changes. Late enrolment into tutorials is handled by STOP 1.

More information on tutorial enrolments including late enrolments is available on the university's website: https://students.unimelb.edu.au/stop1

Tutorial Classes

The tutorials are a fundamental component of the subject. They are designed to practice skills covered during lectures in the previous week.

This semester some of the tutorial classes will be on campus in various computer labs of The Spot building while others will be online (Zoom) in real time. For each tutorial students can download a detailed tutorial handout from the subject Friday before after 12pm. They can also watch a video on the subject website every week on how to use the R / RStudio software.

Each tutorial consists of two components.

PART A: Tutorial questions and exercises to be completed manually and/or with R / RStudio. Detailed explanations, instructions and solutions are provided in the tutorial handout to assist students to do these exercises in their own pace.

Students are strongly advised to read the tutorial handout, to watch the corresponding video on R / RStudio and to attempt the illustrative exercises before the tutorial class, so that they can ask relevant questions and help if needed during their Zoom tutorials. In addition, the tutors might discuss some additional practice exercises to highlight the crucial points of the week.

PART B: Homework exercises and questions for assessment. They are similar to the Part A exercises but they will be organized as weekly Canvas Quizzes.

Students need to submit their answers to these exercises in the relevant Canvas Homework Quiz. The answer for each exercise must be typed in the corresponding box available in the Quiz. If an exercise requires R, the relevant R / RStudio script and printout must be inserted in the same Quiz box below the answer. Please note that handwritten scanned answers and uploaded doc, docx, pdf, jpeg etc. files are not accepted.

There will be 11 tutorial homeworks on weeks 1-11. The solution(s) for the week 1 homework exercise(s) is (are) to be submitted by the week 2 tutorial, the solution(s) for the week 2 homework exercise(s) is (are) to be submitted by the week 3 tutorial, …, the solution(s) for the week 11 homework exercise(s) is (are) to be submitted by the week 12 tutorial.

Private Tutoring Services

The Faculty has become increasingly concerned about the existence of a number of private tutoring services operating in Melbourne that heavily target University of Melbourne students enrolled in FBE subjects.

Students are urged to show caution and exercise their judgement if they are considering using any of these services, and to please take note of the following:

Any claim by any of these businesses that they have a “special” or “collaborative” or “partnership” style relationship with the University or Faculty is false and misleading.

Any claim by a private tutoring service that they are in possession of, or can supply you with, forthcoming University exam or assignment questions or “insider” or “exclusive” information is also false and misleading.

The University has no relationship whatsoever with any of these services and takes these claims very seriously as they threaten to damage the University’s reputation and undermine its independence.

It is also not appropriate for students to provide course materials (including University curricula, reading materials, exam and assignment questions and answers) to operators of these businesses for the purposes of allowing them to conduct commercial tutoring activities. Doing so may amount to misconduct and will be taken seriously. Those materials contain intellectual property owned or controlled by the University.

We encourage you to bring to the attention of Faculty staff any behaviour or activity that is not aligned with University expectations or policy as outlined above.

Assessment

Assessment Overview

Your assessment for this subject comprises the following:

Assessment Task	Due	Weighting
First Assignment (individual or pair)	10am on Monday 23 August	5%
Mid-semester online test (individual)	Any time from 10am 31   August till 3pm 2 September	5%
Second Assignment (individual or pair)	10am on Monday 27 September	5%
Third Assignment (individual or pair)	10am on Monday 18 October	5%
Tutorial Participation and   Homework Exercises (individual)	Weekly (the solutions of at   least 10 out of 11 homeworks   uploaded in due time)	10%
3-hour end-of-semester exam (individual)   Hurdle requirement: To pass this   subject student must pass the   end of semester examination.	During the exam period	70%

Assessment Details

Assignments

Students are required to complete three assignments which make up 15% of the total marks. The assignments will involve undertaking quantitative analyses using a calculator and/or the R / RStudio software packages.

You can complete each assignment by yourself as an “individual assignment” or you can form a group of 2 and submit a “group assignment”.

Students who wish to submit a group assignment have to form and register their own group and both students in a group will receive the same mark. Group registration has a different (i.e. earlier) deadline than the assignment itself. Assignment groups must be registered for each assignment separately even if the composition of the group does not change. Students who fail to register their groups in due time must submit their own individual assignments.

No marks will be allocated if a group assignment is submitted by a group of more than 2 students or if students submit the same assignment as individual assignments.

The assignments will be checked by Turnitin for plagiarism.

Assignment Submission

The assignments will be administered in Canvas. The solutions must be typed in a Word document and together with the relevant R codes and printouts must be uploaded to Canvas as a single PDF file.

Please note that you are required to keep a copy of your assignment after it has been submitted as you must be able to produce it at the request of the teaching staff at any time after the submission due date.

Late Submission and Extension

See the Assignment Extension paragraph in the Policy section of this document.

Mid-Semester Online Test

The mid-semester test will be held during week 6 of the semester. Students can undertake the test at any time of their choosing between from 10am 31 August and 3pm 2 September. The test will be accessible online via the LMS. It will consist of 10 multiple choice questions and 5 true or false question and there will be a time limit of 30 minutes to complete the test.

The test will cover the material presented during the lectures up to the end of week 4 and the tutorials up to the end of week 5, and the relevant prerequisites. Please be aware that the test has a strict time limit and prepare accordingly. You will need to have critical value tables for each of the distributions covered during the lectures. These tables are printed in Appendix C of the Selvanathan textbook (see on the next page) and are provided on LMS too. You will also need a calculator as some calculations may be required to answer certain questions.

Tutorial Participation and Homework Exercises

Tutorial participation and homeworks are worth 10% of the final assessment. To gain this 10% credit, students have to attend their tutorials and make genuine attempts to complete and submit their solutions for at least 10 of the 11 tutorial homeworks in due time.

Students get one tutorial mark on any given week only if they submit their solutions for the previous week tutorial homework exercises in the relevant Canvas Homework Quiz by their next tutorial and attend the tutorial.

Late submissions or requests sent to the subject coordinator or to the tutors will not be considered.

Students with a legitimate reason for not completing and submitting the homework exercises in due time can request to have the tutorial mark for that week transferred to the exam by applying for special consideration within a week of the tutorial class in question. For further details on Special Consideration see the relevant paragraph in the Policy section of this document.

End-of-Semester Exam

A 3-hour end of semester exam, worth 70% of the final grade for this subject, will cover all the material covered during lectures and tutorials throughout the semester. During the exam you will not be asked to use R / RStudio, but you will have to interpret printouts.

This exam will be conducted online during the University's normal end of semester assessment period. The exact details will be provided by the University's administration later in the semester.

Note: Late submissions within 30 minutes after the completion time will attract a 10% penalty of the total maximum mark for the exam, and submissions 30 minutes after the completion time will not be marked.

Hurdle requirement

You must pass the exam to successfully complete the subject.