STATS 707 : Computational Introduction to Statistics


2024 Semester One (1243) (15 POINTS)

Course Prescription

An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.

Course Overview

STATS 707 is a systematic introduction to statistics and data analysis aimed at graduate students from other disciplines who have some computing experience. The course aims to cover the basic toolkit of statistical data analysis as well as an introduction to concepts and philosophical underpinnings of statistical inference, both frequentist and Bayesian. 

Course Requirements

Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162 Restriction: ENGSCI 314, STATS 201, 207, 208, 210, 225

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication

Learning Outcomes

By the end of this course, students will be able to:
  1. Understand and describe the basic concepts of probability and statistical inference and apply them to data analysis problems. (Capability 3, 4 and 5)
  2. Understand and describe a statistical model, its random variables and parameters. (Capability 3 and 4)
  3. Formulate and solve a hypothesis testing or an ANOVA problem. (Capability 3, 4 and 5)
  4. Formulate and solve a linear, multiple or logistic regression problem. (Capability 3, 4 and 5)
  5. Interpret and communicate the output of a statistical model. (Capability 6)


Assessment Type Percentage Classification
Assignments 50% Individual Coursework
Test 50% Individual Test
Assessment Type Learning Outcome Addressed
1 2 3 4 5


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Key Topics

  • Introduction to Probability
  • Introduction to mathematical statistics and statistical inference: point and interval estimation, hypothesis testing, ANOVA, linear/multiple linear regression and generalized linear regression.

Special Requirements

There will be two tests. Both tests are in-person and compulsory. Students will be notified on Canvas about date and time of the tests. 

The details for the assignments will be made available on Canvas.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 150 hours over the course of the semester involved in each 15 point course that they are enrolled in.

For this course, a typical weekly workload includes:

  • 4 hours of lectures
  • 3.5 hours of reviewing the course content
  • 5 hours of work on assignments and/or test preparation

Delivery Mode

Campus Experience

Attendance is required at scheduled activities including the midterm test to receive credit for components of the course.
Lectures will be available as recordings. 
The course will not include live online events.
Attendance on campus is required for the tests.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Reading list, course notes and lecture recordings will be available on Canvas. The students may also be pointed to specific resources for certain topics as and when the need be.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Course content and delivery have been modified and improved based on feedback received in the past. As a result, the course will have greater emphasis on foundational aspects of mathematical statistics and statistical inference, both frequentist and Bayesian. We will spend considerable time explaining the 'why' behind the statistical ideas and concepts and in discussing their applications in real life. Both of these aspects have been especially appreciated by students in the past.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 31/10/2023 10:54 a.m.