Course coordinator
Dr Christiern Rose
Course overview
- Study period
- Semester 1, 2026 (23/02/2026 - 20/06/2026)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Economics School
Basic statistical concepts and techniques such as descriptive statistics, probability concepts, theoretical distributions, inferential statistics (confidence intervals and hypothesis testing) are applied in business and economics.
ECON1310 is an introductory course in quantitative analysis for business and economics. The course covers a variety of techniques applicable to the presentation, interpretation and use of data. The main emphasis is inferential statistics with estimation and hypothesis testing techniques being an important part of the course. Inferential statistics is continued in the simple linear regression topic.
This course is widely regarded as challengingﾠby many students. One reason is that the work is very cumulative. This means that it is exceptionally difficult to catch up if a student gets behind in the work. Success in the subject depends on keeping up-to-date.
As well as covering concepts that are an essential part of the analytical tool box for well-trained professionals, ECON1310 provides a foundation that is critical for success in later statistical courses, particularly ECON2300 Introductory Econometrics.
Course requirements
Assumed background
Algebra from either Queensland High School Maths B, Mathematical Methods, or equivalent.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
Maths B; or Maths C; or MATH1040; or one of Mathematical Methods or Specialist Mathematics (Units 3 and 4, C)
Incompatible
You can't enrol in this course if you've already completed the following:
CHEE2010 or 3010 or CIVL2530 or MINE3214 or PHRM1020 or STAT1201 or 1301 or 2201 or 2203
Course contact
School enquiries
School Enquiries, School of Economics
All enquiries regarding student and academic administration (i.e. non-course content information, e.g., class allocation, timetables, extension to assessment due date, etc.) should be directed toﾠenquiries@economics.uq.edu.au.ﾠ
Enquiries relating specifically to course content should be directed to the Course Coordinator/Lecturer.
Course staff
Lecturer
Dr Christiern Rose
Tutor
Mr Ben Morris
Mr Ethan Bryan
Mr Matt Allison
Mr Aryan Nouri
Miss Si Si Gao
Miss Alison De La Cruz
Mr Lawrence Chen
Mr Barney Lambert
Mr Vivaan Sawhney
Mr Daniil Chechurov
Miss Charlotte Xu
Miss Amber Yeates
Mr Marc Vincent Hagan
Mr Michael Nguyen
Mr Joshua Khan
Mr Harry Talkington
Mr Harsa Kunthara Satrio
Mr Reuben Horne
Senior tutor
Mr Ryan Leung
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Lectures commence in Week 1.
Tutorials commence in Week 2.
Live lectures only will be delivered at St Lucia. Students need to sign-on to attend the live weekly lectures. All students must sign on to a live tutorial group at St Lucia via My Timetable (available through my.UQ dashboard). This is essential in order to attend and pass the required number of In-tutorial Assessment activities that run in tutorials during the semester. Students must enrol in a tutorial group prior to Week 2 and attend the same tutorial group each week. Students must only attend the tutorial they sign-on to for the In-tutorial Assessment activities, otherwise they may be required to leave by the tutor. No tutorial changes will be approved after the UQ Census date.
Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.
Important Dates:
- Public Holidays: Fri 3 April (Good Friday), Mon 4 May (Labour Day).
- Mid-Semester Break: 6 April - 10 April. Semester 1 classes recommence on Mon 13 April.
Students should refer to the timetable prior to the commencement of classes to ensure that they have the most up to date information, as from time to time late room changes may occur.
Aims and outcomes
The aim of the course is for students to develop an ability to apply inferential statistics techniques to independently solve practical problems and to then explain the solutions using everyday language.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Use fundamental statistics terminology.
LO2.
Apply theoretical concepts to construct analytical statistical techniques.
LO3.
Describe the statistical techniques needed to solve particular problem types.
LO4.
Conduct statistical analysis of data so as to draw statistical conclusions.
LO5.
Communicate statistical findings for practical and professional use.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Participation/ Student contribution |
In-Tutorial Assessment
|
40% Best 6 out of 10. |
During a student's signed on tutorial 2/03/2026 - 29/05/2026
At the end of each tutorial. Note that due to public holidays there will be no in-tutorial assessment in weeks 6 and 10. |
| Examination |
End-of-Semester Exam
|
60% |
End of Semester Exam Period 6/06/2026 - 20/06/2026 |
Assessment details
In-Tutorial Assessment
- Identity Verified
- In-person
- Mode
- Written
- Category
- Participation/ Student contribution
- Weight
- 40% Best 6 out of 10.
- Due date
During a student's signed on tutorial 2/03/2026 - 29/05/2026
At the end of each tutorial. Note that due to public holidays there will be no in-tutorial assessment in weeks 6 and 10.
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
Student participation is assessed by tutors who mark students’ individual handwritten solutions to tutor nominated tutorial questions from Week 2 to Week 13. Students write solutions on a blank sheet of paper (without using prewritten answers) that tutors mark after the tutorial. Students are required to attend the same tutorial each week. Note that, due to public holidays, there will be no assessment in week 6 or in week 10.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Submission guidelines
Students must give their hand written tutorial solutions to the tutor at the end of the tutorial to receive a mark. Non submission of any hand written work will result in 0 marks awarded for that in-tutorial assessment activity. Submission of work at a tutorial where a student is NOT signed on may be marked by the tutor. However, students are strongly encouraged to only attend the tutorial they signed on to (to receive their written work back the following week). Note that students can attend a different tutorial during the semester when their tutorial occurs on a public holiday, as outlined in this course profile under Learning Activities.
Deferral or extension
You cannot defer or apply for an extension for this assessment.
Late submission
You will receive a mark of 0 if this assessment is submitted late.
Late submission is not possible. Students can only submit their written work during the relevant tutorial week.
End-of-Semester Exam
- Identity Verified
- In-person
- Mode
- Written
- Category
- Examination
- Weight
- 60%
- Due date
End of Semester Exam Period
6/06/2026 - 20/06/2026
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The End-of-Semester Exam will assess material relating to Lectures 5 to 12 inclusively.
There will be no multiple choice questions. Problem type questions will need to be solved, where all working will be required to be shown, along with possible written explanations of solutions or linking solutions to theoretical ideas.
This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) or Machine Translation (MT) tools will not be permitted. Any attempted use of AI or MT may constitute student misconduct under the Student Code of Conduct.
Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed book examination - specified written materials permitted |
| Materials | A bilingual dictionary |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0% - 29% |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 30% - 46% |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 47% - 49% |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50% - 64% |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65% - 74% |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75% - 84% |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85% - 100% |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
A student's final overall end of semester percentage mark will be rounded to determine their final grade. For example, 64.5% rounds to 65%, while 64.4% rounds to 64%.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Using AI at UQ
Visit the AI Student Hub for essential information on understanding and using Artificial Intelligence in your studies responsibly.
Plagiarism
The School of Economics is committed to reducing the incidence of plagiarism. You are encouraged to read the UQ Student Integrity and Misconduct Policy available in the Policies and Procedures section of this course profile.
The Academic Integrity Module (AIM) outlines your obligations and responsibilities as a UQ student. It is compulsory for all new to UQ students to complete the AIM.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
The following additional information is available on Blackboard:ﾠ
- In-tutorial Assessment information (under Assessment > In-tutorial Assessment).
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Week 1 (23 Feb - 01 Mar) |
Lecture |
Lecture 1: Descriptive Statistics 1 statistical concepts and definitions; data ﾖ types, sources, levels; descriptive & inferential statistics; sampling methods and errors. Learning outcomes: L01, L02, L03, L04, L05 |
Week 2 (02 Mar - 08 Mar) |
Lecture |
Lecture 2: Descriptive Statistics 2 Measures of central tendency, variation and shape for ungrouped data; box and whisker plot; linear combination of random variables; coefficient of correlation. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 1: Descriptive Statistics 1 Tutorial on work covered in Lecture 1. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 3 (09 Mar - 15 Mar) |
Lecture |
Lecture 3: Probability 1 Basic probability concepts, conditional probability. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 2: Descriptive Statistics 2 Tutorial on work covered in Lecture 2. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 4 (16 Mar - 22 Mar) |
Lecture |
Lecture 4: Probability 2 Discrete probability distributions, binomial probability. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 3: Probability 1 Tutorial on work covered in Lecture 3. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 5 (23 Mar - 29 Mar) |
Lecture |
Lecture 5: Normal Distribution Understanding the basis of the normal distribution, interpreting the Standardised Normal Distribution table, computing Z - scores and finding probabilities. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 4: Probability 2 Tutorial on work covered in Lecture 4. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 6 (30 Mar - 05 Apr) |
Lecture |
Lecture 6: Sampling Distributions Sampling distributions of the mean and of the proportion. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 5: Normal Distribution Tutorial on work covered in Lecture 5. Learning outcomes: L01, L02, L03, L04, L05 |
|
Mid-sem break (06 Apr - 12 Apr) |
No student involvement (Breaks, information) |
Mid-semester break Please note, there will be no classes this week. |
Week 7 (13 Apr - 19 Apr) |
Lecture |
Lecture 7: Confidence Intervals 1 Confidence interval estimate for the mean using both Z and t distributions; confidence interval for proportion. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 6: Sampling Distributions Tutorial on work covered in Lecture 6. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 8 (20 Apr - 26 Apr) |
Lecture |
Lecture 8: Confidence Intervals 2 Sample size determination for the mean and proportion; confidence interval for the difference between two means. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 7: Confidence Intervals 1 Tutorial on work covered in Lecture 7. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 9 (27 Apr - 03 May) |
Lecture |
Lecture 9: Hypothesis Testing 1 Hypothesis testing methodology; one and two-tailed tests on the mean using critical value approach (Z and t); types of errors possible. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 8: Confidence Intervals 2 Tutorial on work covered in Lecture 8. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 10 (04 May - 10 May) |
Lecture |
Lecture 10: Hypothesis Testing 2 p-value approach to hypothesis testing; test for proportion; pooled variance test for difference between two means. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 9: Hypothesis Testing 1 Tutorial on work covered in Lecture 9. Note: Monday 6 October is a Public Holiday. Therefore no tutorials or consultations held on that day. Students who normally attend a Monday tutorial session are welcome to attend an alternative tutorial session for this week only. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 11 (11 May - 17 May) |
Lecture |
Lecture 11: Simple Linear Regression 1 SLR model; equation estimation using Excel; coefficient of determination; standard error of the estimate; confidence interval for the slope. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 10: Hypothesis Testing 2 Tutorial on work covered in Lecture 10. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 12 (18 May - 24 May) |
Lecture |
Lecture 12: Simple Linear Regression 2 Assumptions; residual analysis; hypothesis test on slope. Learning outcomes: L01, L02, L03, L04, L05 |
Tutorial |
Tutorial 11: Simple Linear Regression 1 Tutorial on work covered in Lecture 11. Learning outcomes: L01, L02, L03, L04, L05 |
|
Week 13 (25 May - 31 May) |
Lecture |
Lecture 13: Review Review of content from Lectures 1 - 13. |
Tutorial |
Tutorial 12: Simple Linear Regression 2 Tutorial on work covered in Lecture 12. Learning outcomes: L01, L02, L03, L04, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments for Students Policy and Procedure
- AI for Assessment Guide
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.