Students

STAT1371 – Statistical Data Analysis

2025 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff Convenor
Houying Zhu
houying.zhu@mq.edu.au
Convenor
Jack Freestone
jack.freestone@mq.edu.au
Jack Freestone
jack.freestone@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
(HSC Mathematics Extension 1 Band E3 and above or HSC Mathematics Extension 2) or (10cp from MATH1000 or MATH1010-MATH1025) or admission to BActStud or BActStudProfPrac(Hons)
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit provides a comprehensive introduction to modern statistical principles and practices, specifically focusing on data analytical techniques. The primary objective is to develop a solid foundational understanding of the fundamental principles underlying statistical analysis. Throughout the unit, students will gain hands-on experience in data analysis using appropriate statistical software. The unit covers a range of essential topics, including the numerical and visual summarization of sample data, the basics of probability theory, and the concepts of discrete and continuous random variables. Furthermore, this unit covers sampling distributions and their fundamental properties. It also includes hypothesis testing for means, proportions, categorical data, and simple linear regression analysis. To fully benefit from this unit, a solid proficiency in mathematics is assumed, and it is specifically designed for students with a strong mathematical background.

Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Explain the difference between populations and samples, and apply appropriate  statistical techniques to analyse different types of variables
  • ULO2: Understand and apply basic probability concepts to both discrete and continuous variables for solving problems
  • ULO3: Evaluate application of the scientific method through estimation and statistical inference for means, proportions, categorical data and linear regression
  • ULO4: Demonstrate foundational learning skills including active engagement in their learning process.

General Assessment Information

Requirements to Pass this Unit

To pass this unit you need to:

Achieve a total mark equal to or greater than 50% across all assessments.

Hurdle Assessments

  • There is no Hurdle Assessment in this unit.

Attendance and participation

We strongly encourage all students to actively participate in all learning activities. Regular engagement is crucial for your success in this unit, as these activities provide opportunities to deepen your understanding of the material, collaborate with peers, and receive valuable feedback from instructors, to assist in completing the unit assessments. Your active participation not only enhances your own learning experience but also contributes to a vibrant and dynamic learning environment for everyone.

Late Assessment Submission Penalty

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern. 

For any late submission of time-sensitive tasks, such as scheduled tests/exams,  please apply for Special Consideration.

 In this unit, late submissions will be accepted as follows:

  • Skill-based assessment: YES, Standard Late Penalty applies
  • R-based practice test: NO, unless Special Consideration is Granted
  • Final exam: NO, unless Special Consideration is Granted

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive, and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through https://connect.mq.edu.au.

Assessment Tasks

Name Weighting Hurdle Due
Skill-based assessment 20% No 04/04/2025
R based practice test 30% No Week 11
Final Exam 50% No Exam Period

Skill-based assessment

Assessment Type 1: Problem set
Indicative Time on Task 2: 6 hours
Due: 04/04/2025
Weighting: 20%

 

Students will receive a set of problems to solve independently as a take-home assessment.

 
On successful completion you will be able to:
  • Explain the difference between populations and samples, and apply appropriate  statistical techniques to analyse different types of variables
  • Understand and apply basic probability concepts to both discrete and continuous variables for solving problems

R based practice test

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 1 hours
Due: Week 11
Weighting: 30%

 

During the in-class, invigilated R-based practice test, students will demonstrate their practical skills by applying statistical techniques to analyze an assigned dataset.

 
On successful completion you will be able to:
  • Explain the difference between populations and samples, and apply appropriate  statistical techniques to analyse different types of variables
  • Understand and apply basic probability concepts to both discrete and continuous variables for solving problems
  • Evaluate application of the scientific method through estimation and statistical inference for means, proportions, categorical data and linear regression

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 3 hours
Due: Exam Period
Weighting: 50%

 

An invigilated examination held during the University’s formal examination period.

 
On successful completion you will be able to:
  • Explain the difference between populations and samples, and apply appropriate  statistical techniques to analyse different types of variables
  • Understand and apply basic probability concepts to both discrete and continuous variables for solving problems
  • Evaluate application of the scientific method through estimation and statistical inference for means, proportions, categorical data and linear regression
  • Demonstrate foundational learning skills including active engagement in their learning process.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes

Lectures (beginning in Week 1): There are two one-hour lectures each week.

Small-Group Teaching Activities (SGTAs) (beginning in Week 2): Students must register for the SGTA class.

Practicals (beginning in Week 2): Students must register for the Practicals class.

Enrollment can be managed using eStudent at: https://students.mq.edu.au/support/technology/systems/estudent.

Maths Background Quiz

This is a set of questions (pdf format) to allow students to assess whether they have sufficient mathematics in their background for STAT1371. Students are to attempt the quiz in their own time and mark it using the supplied solutions. Only students intending to do an Actuarial Studies degree have STAT1371 as a core unit. For all other students, you should do one of FOSE1015, STAT1170 or STAT1250 based on your degree requirement.

Calculators

Each student will need a small calculator, preferably one that does simple statistical calculations (it should have at least mean and standard deviation capability).  You should bring it to all SGTA classes.  A calculator will also be needed for the final examination.  You will not be permitted to use a programmable calculator or one with a full alpha character set in any examination.

Recommended Textbooks:

  • Diez, D.M., Barr, C.D. & Çetinkaya-Rundel, M. (2012). OpenIntro Statistics. Open Textbook Library (Corporate Author) (ed.), OpenIntro. Available at https://www.openintro.org/book/os/. 
  • Hadley Wickham (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. First Edition. Hadley Wickham & Garrett Grolemund. 
  • Mendenhall, W. Beaver, R.J. and Beaver, B.M. (2019). Introduction to Probability and Statistics. Metric Edition. Cengage Learning Custom P.
  • Richard J. Larsen and Morris L. Marx (2021). Introduction to Mathematical Statistics and Its Applications. 6th edition. Published by Pearson.

Computing and Software

R and RStudio: These are freely available to download from the web and will be used for data analysis in this unit. See Topic 1 for more information. 

Methods of Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to convenors should be placed on the iLearn discussion forums. 

Unit Schedule

The following topics will be covered in this unit.

1.

Sample descriptives

Visual and numerical summaries for both categorical and measured variables.

2.

Probability

Basic probability concepts and applications: events, sets, joint probability, conditional probability, independence and Bayes' Theorem.

3.

Discrete random variables

Evaluation of general expectation and variance.  Properties of sums of random variables and classic discrete distributions. 

4.

Continuous random variables

Evaluation of general expectation and variance (using calculus).  Specific distributions covered: uniform, polynomial-based probability distributions and the normal distribution.

5.

Sampling distributions

Distributions of sample statistics, mean and variance of linear transforms of a random variable, and the Central Limit Theorem. 

6.

Introduction to inference

The scientific method, with particular application to hypothesis testing for a single population mean, one-sample z test, Type I and Type II errors and confidence interval. 

7.

Inference for one population mean

Application of the one-sample t-test and confidence interval.  Investigating normality, particularly normal score plots.

8.

Inference for two population means

The two-sample t-test (and evaluate confidence intervals) for the difference in two population means, and paired t-test.

9.

Correlation and regression

Correlation for two measured variables. Simple linear regression, including estimation and inference for the coefficients, the mean function and prediction intervals, and assumption diagnostics.

10.

Categorical data analysis

Large-sample inference procedures regarding proportions. Hypothesis testing, confidence intervals and inference for categorical data. 

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.

To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

Academic Success

Academic Success provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

To enable students more time to focus on learning, understanding and reflecting on the content of our unit we have revised the assessment structure as follows. There are now only three assessments: skill-based assessment, R based practice test and final exam.  Although no marks are associated with attendance, all activities provide you with key content designed to help you understand the content and complete the assessment. 

We value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page. 

 

Unit information based on version 2025.03 of the Handbook