ECON 1011 - Advanced Economic Analysis I

North Terrace Campus - Semester 2 - 2015

This is a course in statistics that focuses on developing analytical techniques and research skills. Enrolment is restricted to the BEc (Adv) students. By the end of the course, students will have an understanding of the complex nature of quantitative research. Students collaborate to set up a real life economic question, collect appropriate data, analyse the data and report on the question. Topics will include sample surveying, regression and correlation analysis, interval estimation and hypothesis testing.

  • General Course Information
    Course Details
    Course Code ECON 1011
    Course Advanced Economic Analysis I
    Coordinating Unit School of Economics
    Term Semester 2
    Level Undergraduate
    Location/s North Terrace Campus
    Units 3
    Contact Up to 3 hours per week
    Available for Study Abroad and Exchange Y
    Incompatible ECON 1008; STATS 1000; STATS 1005
    Restrictions Only B.Economics (Advanced) students may undertake this course
    Course Description This is a course in statistics that focuses on developing analytical techniques and research skills. Enrolment is restricted to the BEc (Adv) students. By the end of the course, students will have an understanding of the complex nature of quantitative research. Students collaborate to set up a real life economic question, collect appropriate data, analyse the data and report on the question. Topics will include sample surveying, regression and correlation analysis, interval estimation and hypothesis testing.
    Course Staff

    Course Coordinator: Dr Terence Cheng

    Terence Cheng
    Location: Room 4.06, Nexus 10 Tower
    Telephone: 8313 1175
    Email: terence.cheng@adelaide.edu.au
    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    Course learning outcomes

    Upon successful completion of this course, students are expected to be able to:

    1. Describe and apply a variety of graphical and numerical descriptive statistics.

    2. Understand the concepts of randomness and probability; apply probability rules; describe a variety of probability models, and construct measures of random variation.

    3. Understand the concepts of samples, sampling distributions, and estimation; evaluate the properties of point and interval estimators; construct and interpret confidence intervals.

    4. Construct and interpret simple and multiple linear regression models; perform and interpret hypothesis tests; draw appropriate conclusions from models and tests to inform discussion making.

    5. Perform statistical techniques using statistical software; analyse and interpret the results.

    6. Apply statistical knowledge in other courses; recognise and critically evaluate statistics that are observed in everyday life.

    7. Work effectively and inclusively in small groups.

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)
    Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1-7
    The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 1-7
    An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 1, 5-7
    Skills of a high order in interpersonal understanding, teamwork and communication. 6, 7
    A proficiency in the appropriate use of contemporary technologies. 5
    A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 6
    A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 6, 7
    An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 6
  • Learning Resources
    Required Resources
    Textbook
    Sharpe NR, R D De Veaux, and P F Velleman. Business Statistics, 3rd Edition, Global Edition, 2015, Pearson.

    There are earlier editions (e.g. 2014) of this book which are also suitable for this course.

    Calculator
    Students will need a calculator; a basic one that can take squares, square roots would be sufficient. Graphics calculators are allowed but are not necessary.

    Statistical software
    The course will introduce statistical concepts with an emphasis on learning-by-doing using hands-on experience with the use of statistical software, Stata®. This software is available on the workstations in the computer laboratories on campus. You can also access Stata through the ADAPT website either on or off campus using your personal computing device.

    Stata will be used in both lecture and practicals and you are expected to develop familiarity with the software. A good working knowledge of Stata will prepare you for quantitative research in the future. Resources for learning the software will be provided in class.

    Online Learning
    Course materials will be posted on the MyUni course webpage, www.myuni.adelaide.edu.au. Extensive use is made of MyUni; please check the announcements regularly.

  • Learning & Teaching Activities
    Learning & Teaching Modes
    This course involves the use of lectures and practicals.

    Lectures provide an overview of the course content. Students can expect that they will need to study the textbook in order to understand the materials covered.

    The practicals will involve discussions, solving problem sets, and the use of statistical software (Stata) to address statistical problems.

    Before the practicals, students will be expected to have attended and understood the lectures and to have read the relevant chapter(s) from the text book.

    Workload

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Workload
    The workload for this course should consist of:

    Attend lectures 2 hours per week

    Attend practicals 1 hour per week

    Study textbook and lecture notes 4 hours per week

    Prepare homework answers 4 hours per week
    Learning Activities Summary
    Data types and descriptive statistics

    Randomness, random variables and probability

    Sampling and sampling distributions

    Inference—estimation and hypothesis tests for one proportion and one mean

    Correlation and regression

    Topics in multiple regression

    Time series analysis

    Data management and statistical analysis using Stata

    Specific Course Requirements
    The completion of assignment will require access to Stata. This software is available on the workstations in the computer laboratories on campus. You can also access Stata through the ADAPT website either on or off campus using your personal computing device.
  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Final examination (60%)

    Group project (15% due in the practical in week 10)

    Class participation (10%)

    Individual tests (15%)

    Assessment Related Requirements
    To gain a pass, a mark of at least 45% must be obtained on the final examination as well as a total of at least 50% overall.

    Assessment Detail

    Final examination (60%)
    There will be a 3 hour final exam. The exam is comprehensive, meaning it can cover all the topics. Statistical tables will be provided.

    Group project (15% due in the practical in week 10)
    The project is designed for students to demonstrate that they can apply their knowledge of the statistical tools they have learnt to study a real world economic problem. You are required to work on the project in groups of 4-5 students. More details will be provided at the start of the semester. Students are required to present their group project in class on week 12.

    Class participation (10%)
    Homework will be given out every week and these will be covered in practicals in the following week. You are required to prepare the questions before going to the practicals. This preparation is important as it reinforces what you have learnt in lectures. Marks for class participation will be based on your attempt to answer an exercise, and/or contributions to discussions.

    Individual tests (15%)
    There will be a series of MCQ or short written tests in practicals during the semester. There will be 4 of such tests during the semester, and the best 3 marks will count for assessment.

    Because not all of these tests are counted for assessment, no special considerations will be given to students who miss a practical class for medical, compassionate, or any other reason. For any reason, if you attempt less than 4 individual tests, the weight on the final exam will be increased to reflect the shortfall in the individual test component.

    Submission
    Individual tests are done during the practicals or lectures. The group project is to be handed in on week 12, at the end of the student presentations.

    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

    Additional Assessment
    If a student receives 45-49 for their final mark for the course they will automatically be granted an additional assessment. This will most likely be in the form of a new exam (Additional Assessment) and will have the same weight as the original exam unless an alternative requirement (for example a hurdle requirement) is stated in this semester’s Course Outline. If, after replacing the original exam mark with the new exam mark, it is calculated that the student has passed the course, they will receive 50 Pass as their final result for the course (no higher) but if the calculation totals less than 50, their grade will be Fail and the higher of the original mark or the mark following the Additional Assessment will be recorded as the final result.
  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

  • Student Support
  • Policies & Guidelines
  • Fraud Awareness

    Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student’s disciplinary procedures.

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