Department of Statistics


Stage I, II and III courses


Disclaimer

Although every reasonable effort is made to ensure accuracy, this information for the course year (2019) is provided as a general guide only for students and is subject to alteration. All students enrolling at the University of Auckland must consult its official document, the University of Auckland Calendar, to ensure that they are aware of and comply with all regulations, requirements and policies.

 

Stage I courses

STATS 100 Functioning in Statistics


Below description edited in year: 2019

Points: 15

Restrictions: STATS 100 may not be taken with, or after, any other Statistics course. STATS 100 is not available to students who have 14 credits or more in Mathematics and Statistics at NCEA Level 3 or those who have passed Cambridge Mathematics A with an E or better, or Cambridge Mathematics AS with a D or better, or those who have passed International Baccalaureate Mathematics, or equivalent.

Credit: Final exam 30%; Tests 30%; Assignments & Quizzes 30%; Tutorial tasks 5%; Lab tasks 5% OR Final exam 40%; Tests 20%; Assignments & Quizzes 30%; Tutorial tasks 5%; Lab tasks 5%. Must obtain at least 45% in final exam to pass.

Textbooks: Materials will be distributed in class and available from CANVAS.

For Advice: Anna-Marie Fergusson (Email: a.fergusson@auckland.ac.nz | extn: 88434)

Taught: First Semester City

Website: STATS 100 website

The overall goal of STATS 100 is to increase both your confidence and your personal interest in Statistics and Data Science. So if you’ve done a little bit of Statistics study in the past or avoided it completely, and/or think Statistics is boring or difficult, then this course should convince you how awesome working with data really is!

We will focus on how to use data to make decisions by integrating statistical and computational thinking. STATS 100 will develop your conceptual understanding of Statistics and Data Science through active participation in problems using real data, hands-on activities, group work and projects. The course makes full use of appropriate technology and prepares students for further study in Statistics, in particular STATS 101/STATS 101G/STATS 108.

STATS 100 covers similar material to NCEA Statistics but with a greater focus on visualisation, computation (including coding), data manipulation, and modelling approaches. The lectures, tutorials and labs are designed to be interactive and to build on each other over the course. If you are intending to study any subject that requires working with data, this course will help you build strong foundations in the science of learning from data.

Skills and knowledge covered include:

  • Manipulating data and working with a range of data sources, files and structures
  • Selecting appropriate technology and software to explore and visualise data
  • Reasoning critically with data, models and visualisations when forming arguments or making decisions
  • Using mathematical representations and computational techniques in the process of developing models, rules and generalisations
  • Producing written reports that integrate statistical and computational thinking
  • Collaborating with others to design and carry out statistical investigations
  • Describing and applying responsible and ethical practices when obtaining and using data from public sources
  • Considering practical consequences of data-based decisions and clearly communicating uncertainty

Topics: Making predictions, Conducting tests, Building models, Informing decisions.

Top

STATS 101 Introduction to Statistics


Below description edited in year: 2019

Points: 15

Restrictions: STATS 101., 102, 107, 191.

Stats 101G: You cannot take this course for General Education if you have a prior or concurrent enrolment in any of the following subjects: COMPSCI, ENGGEN, ENGSCI, INFOSYS, MATHS, PSYCH or STATS.

Credit: Final Exam = 50%; Test = 20%; Assignments & Quizzes = 30%
or Final Exam = 60%; Test = 10%; Assignments & Quizzes = 30%.
Must obtain at least 50% overall and at least 45% in final exam alone to pass.

Textbooks: Materials produced by the department will be available from the Student Resource Centre and on Canvas. Clickers are available via using web-enabled devices. “Wild & Seber Chance Encounters” is an optional reference text.

For Advice: Emma Wilson (Email: emma.wilson@auckland.ac.nz | extn: 81080), Rhys Jones (Email: rhys.jones@auckland.ac.nz | extn: 85126), Marie Fitch (Email: m.fitch@auckland.ac.nz | extn: 84047), Joss Cumming (Email: j.cumming@auckland.ac.nz | extn: 85756), Anna-Marie Fergusson (Email: a.fergusson@auckland.ac.nz | extn: 88434)

Taught: First Semester City, Second Semester City, Summer School City

Website: STATS 101 website

This course is intended for anyone who will ever have to collect or make sense of data, either in their career or private life. The steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions (statistical inference), and communicating results to others. Technical topics discussed include: types of investigations; data collection; tools for exploring and summarising data; proportions; tools for extrapolating from data (includes confidence intervals to convey uncertainty, randomisation tests, statistical significance, t-tests, and P-values); analysing relationships (includes comparing groups and one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test).

The Department tries to make Statistics come alive by:

  • showing videos that show statistics at work in the real world
  • using class activities to illustrate concepts
  • using small groups to brainstorm ideas or get the answers to exercises
  • using computer demonstrations to clarify ideas, and
  • choosing enthusiastic lecturers who want to see students do well.
  • using web-enabled devices as “clickers” for student interaction.

In fact if your idea of fun is copying formulae off blackboards you probably won't like our courses! 
If you think Statistics 101/108 sounds good but you have always been a bit worried about Maths we offer a variety of help services.

Top

STATS 108 Statistics for Commerce


Below description edited in year: 2019

Points: 15

Restrictions: STATS 101, 102, 107, 191

Credit: Final Exam = 50%; Test = 20%; Assignments & Quizzes = 30%
or Final Exam = 60%; Test = 10%; Assignments & Quizzes = 30%.
Must obtain at least 50% overall and at least 45% in final exam alone to pass.

Textbooks: Materials produced by the department will be available from the Student Resource Centre and on Canvas. Clickers are available via using web-enabled devices. “Wild & Seber Chance Encounters” is an optional reference text.

For Advice: Heti Afimeimounga (Email: h.afimeimounga@auckland.ac.nz | extn: 84934), Anna-Marie Fergusson (Email: a.fergusson@auckland.ac.nz | extn: 88434), Rhys Jones (Email: rhys.jones@auckland.ac.nz | extn: 85126), Marie Fitch (Email: m.fitch@auckland.ac.nz | extn: 84047), Emma Wilson (Email: emma.wilson@auckland.ac.nz | extn: 81080), Joss Cumming(Email: j.cumming@auckland.ac.nz | extn: 85756)

Taught: First Semester City, Second Semester City, Summer School City

Website: STATS 108 website

This course is intended for anyone who will ever have to collect or make sense of data, either in their career or private life. The steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions (statistical inference), and communicating results to others. Technical topics discussed include: types of investigations; data collection; tools for exploring and summarising data; proportions; tools for extrapolating from data (includes confidence intervals to convey uncertainty, randomisation tests, statistical significance, t-tests, and P-values); analysing relationships (includes comparing groups and one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test).

The Department tries to make Statistics come alive by:

  • showing videos that show statistics at work in the real world
  • using class activities to illustrate concepts
  • using small groups to brainstorm ideas or get the answers to exercises
  • using computer demonstrations to clarify ideas, and
  • choosing enthusiastic lecturers who want to see students do well.
  • using web-enabled devices as “clickers” for student interaction.

In fact if your idea of fun is copying formulae off blackboards you probably won't like our courses! 
If you think Statistics 101/108 sounds good but you have always been a bit worried about Maths we offer a variety of help services.

Top

STATS 125 Probability and its Applications


Below description edited in year: 2019

Points: 15

Coreqs: MATHS 108 or MATHS 150 (or equivalent – for more information, speak to our undergraduate course advisers).

Prereqs: Good marks in Year 13 mathematics or university equivalent

Restrictions: If you have already passed STATS 210, you are not allowed to enrol into STATS 125.

Credit: Final exam 50%; test 20%; assignments 18%; tutorials 12% or final exam 60%; test 10%;assignments 18%; tutorials 12%. Must obtain at least 50% overall and at least 45% in the final exam to pass.

For Advice: Marie Fitch (Email: m.fitch@auckland.ac.nz | extn: 84047)

Taught: First Semester City, Second Semester City

Website: STATS 125 website

The course concentrates on probability models and their applications in a variety of fields. Probability models are used in disciplines as varied as commerce and biology (e.g. calculating the probability that a share price will exceed a certain level or the probability that a population will become extinct). Probability underpins both statistics and (stochastic) operations research. The course will cover probability, conditional probability, Bayes' theorem, discrete distributions, expectation and variance, joint and conditional discrete distributions, definition and examples of Markov chains, random walks, hitting probabilities and times, equilibrium distributions. Illustrations will be drawn from a wide variety of applications including finance and economics, genetics, bioinformatics and other areas of biology, telecommunications networks, games, gambling and risk, and forensic science.

Topics studied include: Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including finance and economics; biology; telecommunications, networks; games, gambling and risk.

Top

STATS 150 Lies, Damned Lies, and Statistics


Below description edited in year: 2018

Points: 15

Credit: Final Exam = 50%; Test = 15%; Assignments = 30%; Tutorial tasks = 5%. Must obtain at least 40% in final exam to pass.

Textbooks: Recommended reading: The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics and in Life (2008), by Michael Blastland and Andrew Dilnot 
Seeing through Statistics , 3rd edition (2004), by Jessica M. Utts

For Advice: Stephanie Budgett (Email: s.budgett@auckland.ac.nz | extn: 82346)

Taught: Second Semester City

Website: STATS 150 website

STATS 150 (also available for General Education as STATS 150G) is to prepare anyone, regardless of whether or not they have any background in statistics, to become a critical consumer of statistical information. It will be useful, for example, for aspiring journalists, politicians, political scientists, sociologists, lawyers, public communicators, health personnel, conservationists, environmental scientists, business people, marketers, engineers, and scientists. It examines the uses, limitations, and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing, and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. The interpretation and critical evaluation of statistically-based reports as well as the construction of statistically-sound arguments and reports will be emphasised. Some course material will be drawn from topics currently in the news.

This course teaches you how to critique statistical reporting. It does not, however, teach you how to analyse data. Thus, it alone does not serve as a prerequisite for any of our more advanced courses in statistics, or as the statistical prerequisite for BCom or Psychology.

Topics: Introduction to Media Reports, Surveys and Polls, Experimentation, Risk, Media Reports, Statistical Reasoning.

Coursepack: A coursepack of readings is supplied and is available from the Student Resource Centre in the basement of the Science Centre Maths/Physics Building 303.

Top

Stage II courses

STATS 201 Data Analysis


Below description edited in year: 2018

Points: 15

Prereqs: 15 Points from STATS 101, 102, 108, 191.

Restrictions: You may take only one of STATS 201, 207 and STATS 208

Credit: You may take only one of STATS 201, 207, 208, BIOSCI 209.

Textbooks: Wild & Seber “Chance Encounters: A First Course in Data Analysis and Inference”. Also consult the Mathematics and Statistics Student Resource Centre

For Advice: Yalu Wen (Email: y.wen@auckland.ac.nz | extn: 83214), Russell Millar (Email: r.millar@auckland.ac.nz | extn: 85003), Rachel Fewster (Email: r.fewster@auckland.ac.nz | extn: 83946), Andrew Balemi (Email: a.balemi@auckland.ac.nz | extn: 85713), Renate Meyer (Email: renate.meyer@auckland.ac.nz | extn: 85755)

Taught: Summer School City, First Semester City, Second Semester City

Website: STATS 201 website

The courses STATS 201/8 teach computer based data analysis. They are particularly useful for Business and Economics, and the Biological, Medical and Social Sciences. They are useful for anyone who will do research, or even just read research papers in any discipline where research makes use of statistical analyses.

Topics studied include: Exploratory Data Analysis, the analysis of linear models including simple linear regression of continuous variables and factor variables extended to one-way and two-way analysis of variance and analysis of co-variance, multiple regression, and model selection. This will be extended to generalised linear modelling (i e. Poisson counts and logistic/ binomial regression) and the analysis of contingency table data, along with the analysis of time series data. Aspects of experimental design will be discussed throughout.

Top

STATS 208 Data Analysis for Commerce


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 101, 102, 108, 191

Restrictions: You may take only one of STATS 201, 207 and STATS 208

Credit: Final Exam = 60%; Test = 20%; Assignment = 20% 
or
Final Exam = 70%; Test = 10%; Assignment = 20%. 
Students must obtain at least 50% overall and at least 45% in the final exam to pass.

Textbooks: Wild & Seber "Chance Encounters: A First Course in Data Analysis and Inference". Also consult the Mathematics and Statistics Student Resource Centre.

For Advice: Rachel Fewster (Email: r.fewster@auckland.ac.nz | extn: 83946), Yalu Wen (Email: y.wen@auckland.ac.nz | extn: 83214), Andrew Balemi (Email: a.balemi@auckland.ac.nz | extn: 85713), Russell Millar (Email: r.millar@auckland.ac.nz | extn: 85003)

Taught: Summer School City, First Semester City, Second Semester City

Website: STATS 208 website

The courses STATS 201/8 teach computer based data analysis. They are particularly useful for Business and Economics, and the Biological, Medical and Social Sciences. They are useful for anyone who will do research, or even just read research papers in any discipline where research makes use of statistical analyses.

Topics studied include: Exploratory Data Analysis, the analysis of linear models including simple linear regression of continuous variables and factor variables extended to one-way and two-way analysis of variance and analysis of co-variance, multiple regression, and model selection. This will be extended to generalised linear modelling (i e. Poisson counts and logistic/ binomial regression) and the analysis of contingency table data, along with the analysis of time series data. Aspects of experimental design will be discussed throughout.

Top

STATS 210 Statistical Theory


Below description edited in year: 2019

Points: 15

Coreqs: 15 points from MATHS 208, 250 or equivalent.

Prereqs: 15 points from STATS 125 or equivalent

Restrictions: STATS 225

Credit: Final exam 60%, test 10%, tutorials 8%, assignments 22%. Must obtain at least 50% in the final exam to pass.

Textbooks: Notes distributed in class

For Advice: Ciprian Giurcaneanu (Email: c.giurcaneanu@auckland.ac.nz | extn: 82819)

Taught: First Semester City, Second Semester City

STATS 210 introduces the theory that underlies the statistical methods used in practical statistics courses. It is aimed at students who enjoy maths and are interested in probability and statistics. It is useful for students with interests in Econometrics, Operations Research, Finance, and theoretical aspects of Marketing Research, as well as those who have Maths or Statistics as their main interest. STATS 210 is a prerequisite for STATS 310 and admission to a postgraduate degree in Statistics. Students majoring in Statistics must take either STATS 125 or STATS 210

Topics studied include: Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.

Top

STATS 220 Data Technologies


Below description edited in year: 2018

Points: 15

Prereqs: 15 points in Stage 1 Computer Science or Statistics

Credit: Final exam 60%; test 20%; assignment 15%; labs 5%; must obtain at least 50% overall and at least 50% in final exam to pass.

Textbooks: Introduction to Data Technologies

For Advice: Brendon Brewer (Email: bj.brewer@auckland.ac.nz | extn: 82665), Paul Murrell (Email: p.murrell@auckland.ac.nz | extn: 85392)

Taught: First Semester City

Website: STATS 220 website

This course introduces a variety of computer technologies relevant to storing, managing, and processing data. The course has two aims: to teach software tools specific to the handling of data, and to teach and build confidence with general concepts of computer languages. It is useful for students with interests in applying statistics in business or research environments. Lectures will be reinforced with weekly lab work.

Topics studied include: How to write computer code; publishing data on the World Wide Web (HTML); data description and semantic markup (XML); data storage (file formats, spreadsheets, databases); data management and summary (database queries, SQL); R programming and data manipulation.

Top

STATS 255 Optimisation and Data-driven Decision Making


Below description edited in year: 2019

Points: 15

Prereqs:

  • ENGSCI 211 or STATS 201 or 208, or 
  • a B+ or higher in either MATHS 120 or 130 or 150 or 153 or STATS 101 or 108, or 
  • a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208

Restrictions: ENGSCI255

Credit: Final exam 60%; test 20%; assignment 20% or final exam 80%; assignment 20%, must obtain at least 50% overall and at least 45% in final exam to pass.

Textbooks: Course book purchased from Student Resource Centre.

For Advice: David Smith (Email: dp.smith@auckland.ac.nz | extn: 85390), Geoffrey Pritchard (Email: g.pritchard@auckland.ac.nz | extn: 87400)

Taught: First Semester City, Second Semester City

Website: STATS 255 website

STATS 255 considers a range of practical operations research problems, including effective use of limited or valuable resources such as machines and personnel, understanding queues and simulation. The course is valuable for students interested in Commerce, Statistics, Mathematics, and Computer Science. 
STATS 255 will emphasise the relationship between business and industrial applications and their associated Operations Research models. Computer packages will be used to solve practical problems.

Topics such as: linear programming, transportation and assignment models, network algorithms, queues, inventory models, simulation, analytics and visualisation will be considered.

Top

Stage III courses

STATS 301 Statistical Programming and Modeling using SAS


Below description edited in year: 2017

Points: 15

Prereqs: 15 points from STATS 201, 207, 208, or BIOSCI 209.

Credit: Final exam 60%; coursework 40% (1 test worth 20% and assignments worth 20%), must obtain at least 50% in the coursework and 50% in the final exam to pass

Textbooks: Recommended: The Little SAS Book: a primer (SAS 2003, 3rd edition). This book can be purchased from the Student Resource Centre when in stock.

For Advice: Patricia Metcalf (Email: p.metcalf@auckland.ac.nz | extn: 82317), Mehdi Soleymani (Email: m.soleymani@auckland.ac.nz | extn: 89930)

Taught: Summer School City, Second Semester City

Website: STATS 301 website

One of the key purposes of STATS 301 is to introduce you to the SAS software for the purposes of statistical inference, programming and modelling. SAS is a major commercial statistics package that is used at about 40,000 sites worldwide, and by four million users. We will use SAS as a programming language, and some more advanced features of SAS programming. STATS 301 is designed to be a practical course in the use of SAS in industry, such as, Market Research, Finance and Medicine. To date all the data you have seen has usually been given to you in a form ready for exploration and modelling. This is rarely the case in most day-to-day projects in industry. Here, the emphasis will be on getting data from a “raw and messy” form into a state ready for the data analysis techniques you have learnt, or will learn, at undergraduate level.

Topics studied include: The general SAS programming environment, reading data into SAS, 'Slicing Dicing and Splicing data' and presenting the data in user friendly formats. Statistical Modeling techniques include linear modeling, multivariate ANOVA, tables of counts.

--
SAS Software for work at home use:

SAS WAH can be downloaded within the university at no cost. For further details refer Cecil Notice or talk to your professor.

Top

STATS 302 Applied Multivariate Analysis


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 201, 207, 208 or BIOSCI 209.

Credit: Final exam 50%; Coursework 50% (1 Test worth 20%, Assignments worth 20% and Quizzes worth 10%), must obtain at least 50% in final exam to pass. For advice: Mike Forster (ext 88759). This course covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results.

For Advice: Beatrix Jones (Email: beatrix.jones@auckland.ac.nz | extn: 84790)

Taught: Second Semester City

Website: STATS 302 website

Software-based exploration of multivariate data. Modern extensions to high dimensional and non-normal data.

Topics studied include: Visualisation, Principle components, factor analysis, ordination, cluster analysis, multivariate multiple regression and associated methods. The approach will be largely non-mathematical and practical, with an emphasis on the understanding of the techniques.

Top

STATS 310 Introduction to Statistical Inference


Below description edited in year: 2019

Points: 15

Prereqs: STATS 210 and 15 points from MATHS 208, MATHS 250 or equivalent

Credit: Final exam 55%; mid-term test 15%; assignments 30%. Must obtain at least 50% in the final exam to pass

Textbooks: Rice, J. A. (2007). Mathematical Statistics and Data Analysis, 3rd edition, Duxberry Press. Available from the University Bookshop.

For Advice: Yong Wang (Email: yong.wang@auckland.ac.nz | extn: 84700)

Taught: First Semester City

Website: STATS 310 website

This course follows on from course STATS 210 and provides the theory underlying the statistical methods used in other courses. Many BSc (Hons) Statistics courses use this course as a prerequisite. It is a good course for students with interests in mathematics, econometrics or finance, as well as those who consider their main interest to be statistics.

Topics studied include: Multivariate distributions, marginal and conditional distributions, covariance, conditional expectation, sampling theory, maximum likelihood estimation, likelihood and score functions, Cramér-Rao lower bound, asymptotic optimality of maximum likelihood estimators, construction of confidence intervals, hypothesis testing, power and size of hypothesis tests, Neyman-Pearson lemma, link between hypothesis tests and confidence intervals, general linear models, least squares estimates, theory of estimation and testing for linear models.

Top

STATS 320 Applied Stochastic Modelling


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 125, STATS 210 and 15 points from STATS 201, 207, 208, 220 or BIOSCI 209

Credit: The best of the following two options. Option 1: Assignments (25%), Term test (10%) & Final exam (65%). Option 2: Assignments (25%) & Exam (75%). Must obtain at least 40% in final exam to pass.

Textbooks: Recommended Reading: Kleijnen, J. & van Groenendaal, W., Simulation: A Statistical Perspective (Wiley 1992).

For Advice: Ilze Ziedins (Email: i.ziedins@auckland.ac.nz | extn: 85051), Simon Harris (Email: simon.harris@auckland.ac.nz | extn: 81109)

Taught: First Semester City, Second Semester City

Website: STATS 320 website

This course concentrates on stochastic methods used in operations research, biology etc. It covers the construction, analysis and simulation of stochastic models, as well as some optimisation questions connected with these models. It is valuable for students interested in Business, Economics, Statistics, Mathematics, Computer Science and the Biological Sciences.

Topics studied include: The Poisson process, birth and death processes, queueing theory, simulation, random number generation, variance reduction, and optimisation

Top

STATS 325 Stochastic Processes


Below description edited in year: 2018

Points: 15

Prereqs: B in one of STATS 125, STATS 210 or STATS 320 or 320 and 15 points from MATHS 208, 250, 253.

Credit: Final exam 65%, test 15%, assignments 20%, or if plussage applies, final Exam 75%, test 5%, assignments 20%.

Textbooks: Grimmett, G.R. and Stirzaker, D.R., Probability and Random Processes, (OUP 1992)

For Advice: Simon Harris (Email: simon.harris@auckland.ac.nz | extn: 81109), Jesse Goodman (Email: jesse.goodman@auckland.ac.nz | extn: 88646)

Taught: Second Semester City

Website: STATS 325 website

This course looks at the theory of stochastic processes, showing how complex systems can be built up from sequences of elementary random choices. The course is useful for students with interests in Mathematics, Statistics, Operations Research, Finance and Theoretical Biology.

Topics studied include: Markov chains, random walks, generating functions, branching processes.

Top

STATS 326 Applied Time Series Analysis


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 201, 207, 208, BIOSCI 209 or ECON 221

Credit: Final exam 50%; coursework 50% (1 test worth 15% and assignments worth 25% and in class quizzes and/or weekly Canvas Quiz worth 10%), 50% in final exam to pass.

For Advice: Mike Forster (Email: m.forster@auckland.ac.nz | extn: 88759)

Taught: Summer School City, First Semester City

Website: STATS 326 website

STATS 326 covers Time Series data, with an emphasis on computer based analysis and reporting the results of analyses.

Topics studied include: Time series data, non-stationary time series models, stationary time series models, differencing of non-stationary time series and an introduction to some advanced topics in time series analysis (Multiple Time Series Regression; Arch & Garch; Panel Data; Random Walks, Spurious Regression, Unit Root tests and Co-integrated Time Series Models). The approach will be largely non-mathematical and practical, with an emphasis on applications using R and an appreciation of the problems associated with modelling time series data.

Top

STATS 330 Advanced Statistical Modelling


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 201, 207, 208 or B+ in BIOSCI 209

Credit: Final exam 60%; coursework 40% (1 test worth 20% and assignments worth 20%), must obtain at least 50% in final exam to pass.

Textbooks: Course notes available from the Student Resource Centre.

For Advice: Claudia Rivera Rodriguez (Email: c.rodriguez@auckland.ac.nz | extn: 83608), Thomas Yee (Email: t.yee@auckland.ac.nz | extn: 88811), Arden Miller (Email: a.miller@auckland.ac.nz | extn: 85053)

Taught: Second Semester City, First Semester City

Website: STATS 330 website

The main emphasis of STATS 330 is on analysing data using extensions of the regression methods seen in STATS 201/8. These extensions permit, for example, the building of models for response variables which are not continuous. The main statistical computer package used is R. Students from STATS 210 who have not taken STATS 201/8 will need to do some preparatory reading. STATS 330 is very useful for almost all subjects in Business and Economics, for Operations Research, for any experimental or social science. It is also a useful complement to Computer Science.

Topics studied include: Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, log-linear models and logistic regression models. The graphical exploration of data. Time allowing, we may also cover generalized additive models, including smoothing (e.g., regression splines) as well as negative binomial regression.

The course is based on R and has a significant practical component.

Top

STATS 331 Introduction to Bayesian Statistics


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 201, 207, 208 or BIOSCI 209.

Credit: Final exam 60%, mid-semester test 20% and assignment 20%.

Textbooks: Notes will be available in class

For Advice: Brendon Brewer (Email: bj.brewer@auckland.ac.nz | extn: 82665)

Taught: Second Semester City

Website: STATS 331 website

Bayesian Statistics is all about understanding and modelling uncertainty. Since uncertainty can arise in any field of study, the scope for applications is immense. The paper starts with a brief history of statistics, and shows that the Bayesian paradigm was how statistics was originally applied and how it went out of fashion, to be largely replaced by the frequentist (classical) paradigm. Recently, the Bayesian paradigm has had a renaissance, due in part to the development of MCMC (Markov Chain Monte Carlo) methods that allow the results to be computed quite easily even in complex scenarios. The Bayesian approach will be introduced for discrete distributions using the “Bayes Box”. An unknown parameter is treated as a random variable with a probability distribution that describes our uncertainty. When data are obtained, the probability distribution gets updated to form the latest understanding of the parameter called the posterior. After many of the foundational concepts have been developed using the Bayes Box, the continuous version of Bayes’ theorem will be developed and applied using the software package JAGS. The course will enable a student to perform the kind of analyses encountered in STATS 201/7/8 from a Bayesian perspective.

Topics studied include: Probability and uncertainty, Bayesian updating, parameter estimation, hypothesis testing, MCMC methods, hierarchical models, Bayesian versions of common data analysis tasks.

Top

STATS 340 Design of Surveys And Experiments


Below description edited in year: 2017

Points: 15

Prereqs: 15 points from STATS 201, 207, 208, 210, 225 or BIOSCI 209.

Restrictions: STATS 341 and 351

Credit: Final exam 60%; coursework 40% (1 test 20% and assignments 20%); must obtain at least 50% in final exam to pass.

Textbooks: Lecture notes will be distributed in class.

For Advice: Arden Miller (Email: a.miller@auckland.ac.nz | extn: 85053), Andrew Sporle (Email: a.sporle@auckland.ac.nz | extn: 85948)

Taught: First Semester City

Website: STATS 340 website

The course looks at the design and analysis of two of the most important types of statistical research study, the survey and the designed experiment (eg. how to compare sixteen consumer products when each individual can only compare four, how to produce the most robust paint surface on kitchen appliances). This course is useful for intending statisticians, Operations Research or Management Science professionals, and anyone wanting to do research in an area which uses such studies (eg, Market Research, the Social Sciences, the Biological Sciences, Medicine, Engineering, and Agriculture).

Survey methods including stratified, multistage and cluster sampling; experimental designs including incomplete block, Latin square, split plot, and factorial designs.

Top

STATS 369 Data Science Practice


Below description edited in year: 2017

Points: 15

Prereqs: STATS 220, 201 or 208, 210 or 225

For Advice: Abhinav R Chopra (Email: ar.chopra@auckland.ac.nz | extn: 89621), Thomas Lumley (Email: t.lumley@auckland.ac.nz | extn: 83785)

Taught: Second Semester City

Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.

Top

STATS 370 Financial Mathematics


Below description edited in year: 2018

Points: 15

Prereqs: 15 points in Stage 2 Mathematics and 15 points in Stage 2 Statistics or BIOSCI 209.

Credit: Final exam 75%; coursework 25% (1 test worth 15% and assignments worth 10%) or Final exam 100%, must obtain at least 50% in final exam to pass.

Textbooks: None. Handouts will be distributed.

For Advice: Geoffrey Pritchard (Email: g.pritchard@auckland.ac.nz | extn: 87400), David Smith (Email: dp.smith@auckland.ac.nz | extn: 85390)

Taught: Second Semester City

Website: STATS 370 website

STATS 370 is suitable for Finance majors who want to learn more about the more mathematical aspects of the subject and for Statistics or Mathematics majors wanting to learn about Finance.

Topics studied include: Models for financial returns; pricing of options using binomial models and the Black-Scholes formula; mean-variance portfolio theory; compound interest, annuities,capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.

Top

STATS 380 Statistical Computing


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 201, 207, 208, 220 or BIOSCI 209

Credit: Final exam 75%, coursework 25%. One must obtain at least 50% in the exam to pass the paper.

For Advice: Azam Asanjarani (Email: azam.asanjarani@auckland.ac.nz | extn: 83628), Mehdi Soleymani (Email: m.soleymani@auckland.ac.nz | extn: 89930)

Taught: Second Semester City

Website: STATS 380 website

STATS 380 is designed to provide an introduction to programming with the R programming language. The course will provide a general introduction to programming and discuss the specific techniques which make it possible to use R productively. After successfully completing the course, students should be able to develop new software components for their own use or for use by others.

Topics studied include: Fundamentals of Programming (basic data structures, control-flow and vectorisation, creating new functions); Applications (numerical computation and optimisation, simulation, data processing); and Graphics (visualising data in R).

Top