Course profile

Introductory Statistics for Social Sciences (ECON1310)

Study period
Sem 1 2025
Location
St Lucia
Attendance mode
In Person

Course overview

Study period
Semester 1, 2025 (24/02/2025 - 21/06/2025)
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. There is an emphasis on the use of Excel for analysis and presentation.

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.ﾠSuccess in the subject depends on keeping up-to-date. With this in mind,ﾠeach topic is progressively assessed using various computer managed quizzes throughout the course.

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.

Note: Peer Assisted Study Sessions (PASS) will no longer be offered in this course (from semester 1, 2024).

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

Associate Professor Carl Sherwood

Tutor

Mr Ryan Leung
Mr Oliver Cathcart
Mr Shaun Andersen
Mr Ben Morris
Mr Ethan Bryan
Mr Harley Crozier
Mr Macgregor Sargent
Miss Sachi Das
Mr Luke Palmer
Mr Aryan Nouri
Mr Adrian Alappatt
Mr Hansa Tennakoon
Ms Pippa Sutherland
Mr Daniel Quill
Ms Erin Chang
Ms Stephanie Deeb
Mr Edward Cullen

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.

•ﾠﾠLecture Sign On: Please refer to My Timetable (available via the my.UQ dashboard).

•ﾠﾠTutorial Preferencing: Please refer to My Timetable (available via the my.UQ dashboard) for more information on the tutorial preferencing and allocation process.

Please see the Learning Activities section of this Course Profile for the timetabling implications of public holidays.

Important Dates:

·        Public Holidays: Friday 18 April (Good Friday), Mon 5ﾠMay (Labour Day).

·        Mid-Semester Break: 21ﾠApril - 25ﾠApril. Semester 1 classes recommence on Mon 28ﾠApril.

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

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.

Course overview
Assessment

Assessment

Assessment summary

Category Assessment task Weight Due date
Participation/ Student contribution In-Tutorial Assessment
  • Identity Verified
  • In-person
 25% (Best 7 out of 10 tutorials, from only Tutorial 3 through to Tutorial 12)

During a student's signed on tutorial 3/03/2025 - 30/05/2025

At the end of each tutorial.

Paper/ Report/ Annotation, Project The Islands Research Project
30%

17/04/2025 4:00 pm
Examination Final Exam
  • Hurdle
  • Identity Verified
  • In-person
 45%

End of Semester Exam Period

7/06/2025 - 21/06/2025

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

In-Tutorial Assessment

  • Identity Verified
  • In-person
Mode
Written
Category
Participation/ Student contribution
Weight
25% (Best 7 out of 10 tutorials, from only Tutorial 3 through to Tutorial 12)
Due date

During a student's signed on tutorial 3/03/2025 - 30/05/2025

At the end of each tutorial.

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.

This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and 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.

The Islands Research Project

Mode
Written
Category
Paper/ Report/ Annotation, Project
Weight
30%
Due date

17/04/2025 4:00 pm

Other conditions
Student specific.

See the conditions definitions

Learning outcomes
L01, L02, L03, L04, L05

Task description

The Islands is an online tool that provides a population of virtual humans for students to use as subjects in studies. The research project will require students to use The Islands to select random samples from Islander inhabitants, identify appropriate variables, conduct experiments on the inhabitants and collect relevant data. Students will then need to analyse their data using Excel and submit a 1000-word individual written research report through Turnitin. Full details can be found on Blackboard.

This task has been designed to be challenging, authentic and complex. Whilst students may use AI and/or MT technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct. To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI and MT tools.

Submission guidelines

The research written report must be submitted using the Turnitin Link on Blackboard under Assessment.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Late submission

A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.

Later submission through Turnitin will be possible but penalties will apply as noted.

Final Exam

  • Hurdle
  • Identity Verified
  • In-person
Mode
Written
Category
Examination
Weight
45%
Due date

End of Semester Exam Period

7/06/2025 - 21/06/2025

Learning outcomes
L01, L02, L03, L04, L05

Task description

The Final 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 evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI or MT technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.

Hurdle requirements

Achieve at least 45% on the Final Exam to pass the course.

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

To successfully pass ECON1310, a student needs to satisfy an additional hurdle requirement of receiving at least 45% of the total marks available on the Final Exam. If a student's mark is less than 45% of the total marks available on the Final Exam, the maximum possible grade the student can receive is a grade of 3.

Plagiarism: The School of Economics is committed to reducing the incidence of plagiarism. Further information on plagiarism and how to avoid an allegation of plagiarism is available in this course profile under Policies & Guidelines. The link to the Academic Integrity Module (AIM). It is strongly recommended that you complete the AIM if you have not already done so.

Aims and outcomes
Learning resources

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

  • The Islands Research Project information (under Assessment > The Islands Research Project).
  • In-tutorial Assessment information (under Assessment > In-tutorial Assessment).
  • Computer Managed Learning (CML) informationﾠ(under Assessment > CML Quizzes > CML Administrative Folder).
Assessment
Learning activities

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

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Learning period Activity type Topic
Week 1

(24 Feb - 02 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

(03 Mar - 09 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

(10 Mar - 16 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

(17 Mar - 23 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

(24 Mar - 30 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

(31 Mar - 06 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
Week 7

(07 Apr - 13 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

(14 Apr - 20 Apr)
Lecture

Lecture 8: Confidence Intervals 2

Sample size determination for the mean and proportion; confidence interval for the difference between two means.

Note: Friday 18 April is a Public Holiday. There will be no lecture that day. However, all students are expected to work through the online lecture video segments on Blackboard for this lecture. Students must become familiar with the content as it is assessable.

Learning outcomes: L01, L02, L03, L04, L05
Tutorial

Tutorial 7: Confidence Intervals 1

Tutorial on work covered in Lecture 7.

Note: Friday 18 April is a Public Holiday. Therefore no tutorials or consultations held on that day. Students who normally attend a Friday tutorial session are welcome to attend an alternative tutorial session for this week only.

Learning outcomes: L01, L02, L03, L04, L05
Mid-sem break

(21 Apr - 27 Apr)
No student involvement (Breaks, information)

Mid semester Break

Please note, there will be no classes this week.
Week 9

(28 Apr - 04 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

(05 May - 11 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 5 May 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

(12 May - 18 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

(19 May - 25 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

(26 May - 01 Jun)
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
Learning resources
Policies and procedures

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:

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.

Learning activities