Department of Statistics


Postgraduate course descriptions


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.

STATS 701 Advanced SAS Programming


Below description edited in year: 2019

Points: 15

Prereqs: STATS 301, STATS 330

Credit: Assignments = 40%; workshops = 40% (10 workshops for n reports handed in, the best n-1 of these will contribute equal marks.), and 20% for the final test. You must obtain at least 30% out of the 60% available in the assignments and test, and 20% in the weekly workshops to pass.

Textbooks: There is no required text, but the introductory SAS text, The Little SAS book: A Primer , by Delwiche and Slaughter, which was used for STATS 301 is highly recommended.

For Advice: Mike Forster (Email: m.forster@auckland.ac.nz | extn: 88759), Peter Mullins (Email: pr.mullins@auckland.ac.nz | extn: 88275)

Taught: First Semester City

Website: STATS 701 website

Introduction 
One of the key purposes of STATS 701 is to advance your knowledge of SAS software for the purposes of data merging, transformation and re-shaping. SAS is a major commercial statistics package that is used at about 40,000 sites worldwide, and by 4 million users. We will use SAS as a programming language, and some more advanced features of SAS programming (arrays, macros, SQL etc ) will be covered. 

STATS 701 is designed to be a practical course in the use of SAS in industries such as Medicine, Market Research and Finance.

Data steps Dealing with large datasets Macros SQL Data visualization Data mining Report generation, reporting statistical results.

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STATS 705 Official Statistics


Below description edited in year: 2018

Points: 15

For Advice: Andrew Sporle (Email: a.sporle@auckland.ac.nz | extn: 85948), Rhys Jones (Email: rhys.jones@auckland.ac.nz | extn: 85126)

Taught: Second Semester City

Website: STATS 705 website

STATS 705 examines a variety of issues relating to official statistics. It will be taught in cooperation with lecturers from Victoria, Waikato, AUT and Canterbury Universities. This course is being taught via video conference with other New Zealand universities, with staff and students from Waikato, Victoria and Otago also participating. To view the study material and guide, which is also relevant to STATS 705, see Official Statistics 2018 course website

Topics studied include: Official statistics, data access, data quality, demographic and health statistics, other social statistics, economic statistics, analysis and presentation, case studies in the use of official statistics. 


For 2018 - STATS 705 is taught by video-conferencing. We will be following the Victoria University semester 2 schedule. That means we have lectures on Wednesdays from 4 pm to 6 pm in 303-310. The dates are as follows: 18 July to 23 August and 13 September to 10 October. 

Lecture dates
1 18-Jul
2 25-Jul
3 1-Aug
4 8-Aug
5 15-Aug
6 22-Aug
Break 29-Aug
7 5-Sep
8 12-Sep
9 19-Sep
10 26-Sep
11 3-Oct
12 10-Oct

Course website link: https://flexiblelearning.auckland.ac.nz/official-statistics/

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STATS 707 Computational Introduction to Statistics


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from 101, 108; 15 points from COMPSCI 101, MATH 162

Restrictions: 201,208,210,225

For Advice: Beatrix Jones (Email: beatrix.jones@auckland.ac.nz | extn: 84790), Mehdi Soleymani (Email: m.soleymani@auckland.ac.nz | extn: 89930)

Taught: First Semester City

A systematic introduction to statistics and data analysis aimed at graduate students from other disciplines who have some computational experience. The course aims to cover most of the content of 201 and 210.

Point and interval estimation; hypothesis testing; two-sample and k-sample comparisons; linear regression.

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STATS 708 Topics in Statistical Education


Below description edited in year: 2019

Points: 15

Credit: There is no final examination. Essay 50%; Journal 20%; Seminar 20%; Task 10%.

Textbooks: There is no set text. A reading list and papers for discussion will be provided.

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

Taught: First Semester City

Website: STATS 708 website

Statistics education is an exciting field of research. This course covers a wide range of New Zealand and international research in statistics education at the school and tertiary level. The focus will be on how probability, statistical investigations, and statistical literacy can be effectively taught and learned. Consideration of issues involved in learning statistics and probability will be examined through research literature discussions, practical applications for the classroom including computer activities, and lectures. By the end of STATS 708 all students should have aimed to produce a scholarly paper on an aspect of statistics education. This will involve a literature review on that aspect; the writing of the paper to a publishable standard; and a presentation of that paper to an audience. In addition, students should be able to state, and argue coherently for, their perspective on statistics education: how and why specific teaching and learning strategies should be used; the implications of research for teaching and learning; the relationship between statistical learning and assessment; and so forth.

Topics studied include: Probability – decision making under uncertainty, students’ probabilistic conceptions; Statistical Reasoning – students’ understanding of specific statistics content such as averages, distributions, graphs; Statistical Investigations – key ideas underlying data-based enquiry; Statistics and Technology – data analysis and probability modelling software, developing students’ statistical and probabilistic concepts such as inference using technology and visual reasoning learning approaches; Statistical Literacy; Statistics in the Curriculum – conceptual development pathways and assessment.

STATS 708 will involve lectures, practical tasks, and discussion. Each session will require pre-reading of papers and/or pre-viewing of web-based talks and/ or a practical exercise.

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STATS 710 Probability Theory


Below description edited in year: 2018

Points: 15

Prereqs: STATS 310 or 320 or 325, (or MATHS 332 and permission of lecturer)

Credit: Final exam 60%, assignments 40%.

Textbooks: "Knowing the Odds: An Introduction to Probability" by John Walsh

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

Taught: Second Semester City

Website: STATS 710 website

This course will provide an introduction to probability theory, and is recommended for graduate students interested in statistical theory, stochastic modelling, quantitative finance, probability, statistical physics, and analysis. The course will cover: 1. The axiomatic definition of probability, associated set theory, and independence. 2. Random Variables and Vectors, expectation, conditional expectation, moments, and characteristic functions. 3. Limit theorems, including the “fundamental theorems of statistics” (the law of large numbers, and the central limit theorem). 4. Other topics chosen by the instructors.

Topics studied include: Probability with sigma-fields and measurable spaces. Borel-Cantelli Lemmas and 0-1 laws. Random variables, expectation and condition expectation. Moment generating functions and characteristic functions. Sequences of independent random variables, the Law of Large Numbers, and the Central Limit Theorem.

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STATS 720 Stochastic Processes


Content to come | see Calendar.

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STATS 721 Special Topic in Applied Probability


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.

Restrictions: STATS 325

Credit: Exam 60%, Test 15%, Assignments 25%, or if plussage applies, Exam 70%, Test 5%, Assignments 25%.

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 721 website

STATS 721 is taught with STATS 325. This course looks at the theory of stochastic processes, showing how complex systems can be built up from sequences of elementary random choices. STATS 721 is useful for students with interests in Mathematics, Statistics, Operations Research, Finance and Theoretical Biology.

Topics studied include: Generating functions, branching processes, discrete-time Markov chains, random walks. In addition, STATS 721 students conduct independent study of more advanced topics or processes.

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STATS 722 Financial Mathematics


Below description edited in year: 2018

Points: 15

Prereqs: 15 points in Stage 2 Statistics and 15 points in Stage 2 Mathematics

Restrictions: STATS 370

Credit: Final exam 60%; coursework 40% (1 test worth 10% and assignments worth 30%), must obtain at least 50% in final exam to pass.

Textbooks: None. Handouts will be given out

For Advice:  Geoffrey Pritchard (Email: g.pritchard@auckland.ac.nz | extn: 87400), Tanya Evans (Email: t.evans@auckland.ac.nz | extn: 88783)

Taught: Second Semester City

Website: STATS 722 website

This course includes the material in STATS 370. It is intended for postgraduate students who have not already passed STATS 370.

STATS 722 is an extended version of STATS 370. It uses mathematical, statistical and stochastic methods in finance. It covers mean-variance portfolio theory, interest rate theory, basic derivative securities including the derivation of the Black-Scholes option pricing formula, and hedging strategies. The course is suitable for students of statistics, finance, accounting, economics or mathematics.

Topics studied include:Basic probability, investment portfolios, mean, variance and covariance of returns, minimum variance portfolio, portfolio possibilities regions and their properties, the efficient portfolio frontier, riskless lending and borrowing, short sales, single index structural models. An introduction to stochastic differential equations and their solution, simple and compound interest, effective and nominal interest rates, accumulation factors, force of interest, equivalent interest rates, present value, basic compound interest functions, equation of value for a sequence of transactions, internal rate of return, annuities, loan schedules. Stocks, futures contracts, European and American call and put options, arbitrage, Brownian motion, Ito processes, Ito’s lemma, geometric Brownian motion, put-call relationships, Black-Scholes analysis and pricing formulae, risk-neutral valuation, pricing American options, the Cox-Ross-Rubinstein binomial model for stock prices.

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STATS 723 Stochastic Methods in Finance


Below description edited in year: 2018

Points: 15

Prereqs: STATS 210 or equivalent, or with consent of lecturer

Textbooks: N. Bingham and R. Kiesel, "Risk-neutral valuation - pricing and hedging of financial derivatives".L. Clewlow and C. Strickland, "Implementing derivatives models".

For Advice: Jesse Goodman (Email: jesse.goodman@auckland.ac.nz | extn: 88646), Geoffrey Pritchard (Email: g.pritchard@auckland.ac.nz | extn: 87400)

Taught: First Semester City

Website: STATS 723 website


STATS 723 is a course in mathematical finance. We will use the techniques of probability and stochastic processes to study derivative securities that are traded in financial markets. The course is suitable for students of statistics, finance, accounting, economics, or mathematics


Topics studied include: Probability and background - Random variables, distributions, equivalent measures. Essential notions - financial derivatives, arbitrage, replicating portfolios, risk-neutral pricing, complete markets. Martingales. Mathematical finance in discrete time -equivalent martingale measures, Fundamental Theorem of Asset Pricing, Cox-Ross-Rubinstein model. Option pricing, including path-dependent options. Continuous-time stochastic processes - Brownian motion, stochastic integral, Ito's lemma, stochastic differential equations. Mathematical finance in continuous time - Fundamental Theorem of Asset Pricing in continuous time. The Black-Scholes world. Hedge sensitivities ("Greeks"). Further topics - a selection from: volatility estimation, volatility smiles; risk management and hedging, risk measures (VaR and CVaR); multi-factor and other models, Monte Carlo simulation; incomplete markets.

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STATS 726 Time Series


Below description edited in year: 2018

Points: 15

Prereqs: STATS 210 and either STATS 320 or STATS 325. STATS 201/8 is recommended

Credit: 4 assignments worth 40%, final exam = 60%

For Advice: Shanika Wickramasuriya (Email: s.wickramasuriya@auckland.ac.nz | extn: 81083)

Taught: Second Semester City

STATS 726 provides a general introduction to the theory of time series and prediction including stationary processes, moving average and autoregressive (ARIMA) models, modelling and estimation in the time domain, seasonal models, forecasting, spectral analysis and bivariate processes. This foundation course at postgraduate level is particularly suitable for students in economics and finance, and in the engineering and physical sciences.

Specific topics covered include: linear processes; ARMA models; inference and prediction for time series models; spectral analysis of time series; inference in the frequency domain.

Textbooks: Notes distributed in class

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STATS 727 Special Topic in Time Series


Below description edited in year: 2018

Points: 15

Prereqs: STATS 201 or STATS 208, and MATHS 108 or equivalent

Restrictions: STATS 326

Credit: Final Exam 50%, coursework 50% (In class quizzes and/or weekly Canvas Quiz 10%, Assignments 25%, Test and Research Essay 15%); must obtain at last 50% in the final examination to pass.

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

Taught: First Semester City

Website: STATS 727 website

This course includes the material in STATS 326. 
It is intended for postgraduate students who have not already passed STATS 326.

STATS 727 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.

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STATS 730 Statistical Inference


Below description edited in year: 2018

Points: 15

Prereqs: STATS 310 (or 732)

Credit: Final exam 70%, midterm 20%, assignments 10%; or 100% final exam.

Textbooks: Course notes from "Maximum Likelihood Estimation and Inference" (Millar, 2011) will be used.

For Advice: Ben Stevenson (Email: ben.stevenson@auckland.ac.nz | extn: 88474)

Taught: Second Semester City

Website: STATS 730 website

STATS 730 gives you general-purpose skills that are required by many employers of statistical graduates. It will enable you to model real data, using likelihood-based statistical inference under the frequentist paradigm. It provides the tools and skills used by many other graduate courses on offer in this department, and it gives exposure to statistical programming in both R and the advanced optimizer TMB.

STATS 730 begins with a revisit of the basic notions of likelihood, the notation that will be used throughout, and a look at the place of likelihood-based methods in statistical inference. We look at simple and not-so-simple (e.g., finite mixture model) iid examples. The essential properties and tools of maximum-likelihood inference are then presented. Maximum likelihood is then applied in a wide variety of settings with examples in both R and TMB where needed. After covering the theory and practice of generalized linear models the course concludes by looking at mixture models, including nonlinear and generalized linear mixed models.

STATS 730 is focused on application but some theory is needed to justify and motivate the methodology that is used. This will require that the student is comfortable with the concepts, statistical properties, and theory that is presented in STATS 310 (or 732). Students will need to have a good grasp of basic calculus and be comfortable with partial derivatives and Taylor series expansions. It will also be assumed that students are comfortable with basic programming logic (e.g., for-loops) and data manipulation.

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STATS 731 Bayesian Inference


Below description edited in year: 2018

Points: 15

Prereqs: Sound knowledge of STATS 210 and STATS 331, and familiarity with the material in STATS 310 and STATS 330 is an advantage.

Credit: 20% coursework, 20% test, 60% final exam

Textbooks: Recommended and on desk copy at Short Loans or as ebooks
"Bayesian Computation with R" by Jim Albert
"Bayes and Empirical Bayes Methods for Data Analysis" by Carlin and Louis
"Bayesian Data Analysis" by Gelman, Carlin, Stern, and Rubin
"Bayesian Modeling Using WinBUGS" by Ioannis Ntzoufras
"Bayesian Statistics: An Introduction" by Peter Lee

For Advice: Renate Meyer (Email: renate.meyer@auckland.ac.nz | extn: 85755), Brendon Brewer (Email: bj.brewer@auckland.ac.nz | extn: 82665)

Taught: First Semester City

Website: STATS 731 website

STATS 731 is a graduate course in Bayesian inference starting from first principles and building on the material in STATS 331 with major emphasis on Bayesian methods in applied data analysis. Over the last decade, the Bayesian approach has revolutionised many areas of applied statistics such as biometrics, econometrics, market research, statistical ecology and physics. Although the Bayesian approach dates back to the 18th century, its rise and enormous popularity today is due to the advances made in Bayesian computation through computer-intensive simulation methods. Knowledge of Bayesian procedures and software packages will become indispensable for any career in Statistics. We will be using the software package R, JAGS, and WinBUGS/OpenBUGS for Bayesian computation. This course will introduce the theory of Bayesian inference and put strong emphasis on modern, applied Bayesian data analysis.

Topics covered include: the Bayesian approach, conjugate distributions, specification of prior distributions, Likelihood Principle, techniques for posterior computation computation (incl. Laplace approximations, simulation techniques, rejection and importance sampling, Markov chain Monte Carlo methods), Bayesian linear and nonlinear regression models, hierarchical models, dynamic models, approaches to model comparison and selection, applied posterior predictive model checking.

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STATS 732 Topic in Statistical Inference


Below description edited in year: 2018

Points: 15

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

Restrictions: STATS 310

Credit: Final exam 70%; mid-term test 15%; assignments 15%. Must obtain at least 50% in the final exam to pass

Textbooks: None prescribed, but a reading list will be given out

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

Taught: First Semester City

Website: STATS 732 website

This course includes the material in STATS 310. 
It is intended for postgraduate students who have not already passed STATS 310. 

Students will attend lectures and tutorials for STATS 310. Additional material on Decision Theory and Bayesian Inference will be covered in STATS 732 workshops and tutorials.

Topics studied include:

Maximum likelihood estimation, likelihood and score functions, maximum likelihood estimates, Cramér-Rao lower bound, asymptotic optimality of maximum likelihood estimates, construction of confidence intervals. Multivariate Distributions, joint, marginal, and conditional distributions, vector random variables, variances and covariances, conditional means and variances, maximum likelihood estimates for multivariate parameters. 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 Decision theory and Bayesian inference, decision rules, loss functions, risk functions, minimax rules, Bayes rules.

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STATS 740 Sample Surveys


Below description edited in year: 2019

Points: 15

Prereqs: STATS 340 or 341 or equivalent

Credit: Examination 70%, assignments 30%(2 assignments)

Textbooks: Survey Methodology. Robert M Groves (ed), Wiley, 2004. T. Lumley,Lu Ann Aday & L.J. Cornelius, Designing and Conducting Health Surveys. 3rd. Edition, Jossey-Bass, 2006
T. Lumley, Complex Surveys: a guide to analysis using R, Wiley Interscience, 2009

For Advice: Claudia Rivera Rodriguez (Email: c.rodriguez@auckland.ac.nz | extn: 83608)

Taught: Second Semester City

Website: STATS 740 website

STATS 740 is a course in the design, management and analysis of data from sample surveys, with particular emphasis on applications to the health sector.

Topics studied include: Revision of statistical aspects of sampling theory. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Analysis: computer software, weighting, special features of survey data. Dissemination.

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STATS 741 Special Topic in Sampling


Below description edited in year: 2018

Points: 15

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

Restrictions: STATS 340, 341, 351

Credit: Final exam 50%; assignment and laboratories 30% and test 20%

Textbooks: Lecture workbook will be distributed in class

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

Taught: First Semester City

Website: STATS 741 website

This course includes the material in STATS 340. It is intended for postgraduate students who have not already passed STATS 340, STATS 341 or STATS 351. STATS 741 looks at the design and analysis of sample surveys, including some of the theory behind the methods

Topics studied include: Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs.

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STATS 747 Statistical Methods in Marketing


Below description edited in year: 2018

Points: 15

Prereqs: STATS 210 or equivalent. Some data analysis experience (as in STATS 301, 330) and knowledge of SAS or R/Splus is expected.

Credit: Assignments 50%, project 1= 20% and project 2= 30%.

For Advice: Andrew Balemi (Email: a.balemi@auckland.ac.nz | extn: 85713)

Taught: Second Semester City

Website: STATS 747 website

STATS 747 discusses statistical issues that arise and statistical techniques that are frequently used in market research. The emphasis will be on making the commonly used techniques useful to people undertaking market research or who have marketing problems – therefore effective communication between the statistical ‘world’ and the marketing ‘world’ will be a key aspect of this course. Real, recent, examples will be used throughout this course.

Topics studied include: brand choice and models, conjoint analysis, experimental design, sampling, weighting, imputation and variance estimation, advertising media models and marketing response models, and applications of generalised linear models, data mining and multivariate analysis in market research, example, segmentation.

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STATS 760 A Survey of Modern Applied Statistics


Below description edited in year: 2018

Points: 15

Prereqs: STATS 310, 330

Credit: 100% Coursework. Assessment will be based upon seminars, journals, tests and assignment work.

Textbooks: Modern Applied Statistics with S-PLUS (3rd or 4th ed.) by W.N. Venables and B.D. Ripley

For Advice: Andrew Balemi (Email: a.balemi@auckland.ac.nz | extn: 85713)

Taught: First Semester City

Website: STATS 760 website

The aim of this course is twofold: to give students a fairly comprehensive survey of modern applied statistics, and give some training in how to find out details of statistical techniques that are unfamiliar. If you are working in statistics after graduation, you will have to teach yourself a lot of new techniques. This course is designed to give you practice in doing this. Thus, the course is rather different from most postgraduate courses, in that there will be very few lectures. Rather, the course will be tutorial based. The course material will be taken from the book “Modern Applied Statistics with S-Plus” by W.N. Venables and B.D. Ripley.

Topics studied may include: generalised linear models; robust statistics; nonlinear models; random and fixed-effects models; modern regression; survival analysis; multivariate analysis; tree-based methods; time series; spatial statistics; classification.

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STATS 761 Mixed Models


This course is no longer being taught in 2019.

For course details | see Calendar.

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STATS 762 Statistical Modelling


Below description edited in year: 2018

Points: 15

Prereqs: STATS 210 or 225; 15 points from STATS 201, 207, 208 or B+ in BIOSCI 209

Restrictions: STATS 330

Credit: Final exam 60%, tests 20%, assignments 20%

For Advice: Arden Miller (Email: a.miller@auckland.ac.nz | extn: 85053), Abhinav R Chopra (Email: ar.chopra@auckland.ac.nz | extn: 89621)

Taught: First Semester City

Website: STATS 762 website

This course includes the material in STATS 330. It is intended for postgraduate students who have not already passed STATS 330.

STATS 762 is an extended version of STATS 330. The main emphasis of this course is on analysing data using extensions of the regression methods seen in STATS 201/7/8. These extensions permit, for example, the building of models for response variables which are not continuous. The main statistical computer package used is R. Regression modelling is fundamental to statistics and data science. This course should be very useful for almost all subjects in Business and Economics, for Operations Research, for any experimental or social 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, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression.

* The course uses a mixture of lecture and lab exercises. Students should bring their own laptop where possible. (and contact the instructors if this is not possible)

* This course was revised substantially in 2016; information from earlier years may be out of date.

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STATS 763 Advanced Regression Methodology


Below description edited in year: 2019

Points: 15

Prereqs: STATS 330

Restrictions: STATS 762

For Advice: Thomas Lumley (Email: t.lumley@auckland.ac.nz | extn: 83785)

Taught: First Semester City

Website: STATS 763 website

Course Description
This course differs from STATS 762 in assuming prior knowledge of generalised linear models and linear algebra. It is an advanced course in regression modelling, covering theory, computation, and practice

Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.

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STATS 766 Multivariate Analysis


Content to come | see Calendar.

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STATS 767 Topics in Multivariate Analysis


Below description edited in year: 2018

Points: 15

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

Restrictions: STATS 302

Credit: 40% exam, 20% project, 40% Coursework (15% Test, 20% Assignments, 5% quizzes). Must obtain 50% in final exam to pass.

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

Taught: Second Semester City

Website: STATS 767 website

This course includes the material in STATS 302. It is intended for postgraduate students who have not already passed STATS 302. STATS 767 covers the exploratory analysis and modeling of multivariate data, with emphasis on the use of statistical software and reporting of results.

Topics studied include: Techniques for data display, dimension reduction and ordination, cluster analysis, multivariate regression and Analysis of Variance (MANOVA), Canonical Correlation, and Redundancy Analysis. The approach will be largely non-mathematical and practical, with an emphasis on the understanding of the techniques.

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STATS 768 Longitudinal data analysis


Content to come | see Calendar.

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STATS 769 Data Science Practice


Below description edited in year: 2018

Points: 15

For Advice: Paul Murrell (Email: p.murrell@auckland.ac.nz | extn: 85392)

Taught: Second Semester City

STATS 769 is intended to provide students with computing skills involved in the acquisition and manipulation of large and/or complex data sets. A secondary aim is to give students practice in applying data mining techniques; there will be an emphasis on practice rather than theory.

data mining tools, working with database, parallel computing, and large memory computing.

The course will assume a certain amount of basic computing knowledge, such as might be obtained by completion of STATS 220 , STATS 380 or STATS 779. The course will be assessed on assignment work and a final exam.

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STATS 770 Introduction to Medical Statistics


Below description edited in year: 2018

Points: 15

Prereqs: STATS 20x and 210

Textbooks: Notes distributed in class

For Advice: Yalu Wen (Email: y.wen@auckland.ac.nz | extn: 83214)

Taught: First Semester City

Website: STATS 770 website

STATS 770 provides a broad introduction to ideas of importance in medical statistics, such as measures of risk, basic types of medical study, causation, ethical issues, and censoring, together with a review of common methodologies. The goal is to go from questions about health to quantitative questions about data, and, potentially, to answers.

Topics studied include: summaries of risk and health and their implications, confounding and causation, screening and diagnostic testing, estimation and study design.

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STATS 773 Design and Analysis of Clinical Trials


Below description edited in year: 2018

Points: 15

Credit: Final exam= 40%, project= 40%, assignments= 20%

For Advice: Thomas Lumley (Email: t.lumley@auckland.ac.nz | extn: 83785)

Taught: Second Semester City

Website: STATS 773 website

STATS 773 focuses on the question of how to tell whether an intervention works. The course covers statistical, scientific, ethical, and procedural issues in the design, conduct, analysis, and reporting of randomized trials, primarily in clinical medicine and public health. Current standards for clinical trials are the result both of basic theoretical principles and of bitter practical experience, so both theory and case studies will be important in the course.

The course project is an independent reading project resulting in a short paper and a poster.

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STATS 779 Professional Computing Skills for Statisticians


Below description edited in year: 2018

Points: 15

Credit: Worksheets after each lecture 20%, five assignments 20%, final test 60%. There is no final exam.

For Advice: Shanika Wickramasuriya (Email: s.wickramasuriya@auckland.ac.nz | extn: 81083)

Taught: First Semester City

Website: STATS 779 website

STATS 779 is intended to provide students with the skills they will need to undertake many routine tasks as a professional statistician, either in industry or academia. These include text editing, data manipulation, data storage and retrieval, data processing, document preparation and typesetting, presentation skills, spreadsheet skills and advanced graphics. The course will involve gaining familiarity with a wide range of (mostly free) software packages with an emphasis on practice rather than theory. Classes will be carried out in the computer laboratory and we envisage that students will spend a large amount of the lecture time doing aided computing tutorial work. The course will be assessed on the work completed during those computing sessions, five assignments and a final test which in 2017 is expected to be a four hour test in a computer laboratory. There will be no final exam.

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STATS 780 Statistical Consulting


Below description edited in year: 2019

Points: 15

Prereqs: STATS 330 and at least one of STATS 340, 341, 351 or permission of HoD or Graduate Officer

Restrictions: BIOSCI 744

For Advice: Peter Mullins (Email: pr.mullins@auckland.ac.nz | extn: 88275)

Taught: Second Semester City

Website: STATS 780 website

This course is designed to give students training in statistical consulting, ie, applying statistical methods to practical research problems in other disciplines. There are two components to STATS 780. The first mostly consists of lectures and assignments. The second component of the course focuses on a group project; small groups of students will discuss with researchers and other clients the design and analysis of quantitative investigations, using the lecturer and other staff members as additional resources. The course is not designed to teach students specific statistical techniques, but focuses instead on the other skills required for statistical consulting. Students will be assessed on their ability to present both written and verbal reports. This will include a seminar based on the group project to be delivered to the client and members of the Statistics Department. It is assumed that students will have passed STATS 330 and STATS 340. There is no final exam.

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STATS 782 Statistical Computing


Below description edited in year: 2018

Points: 15

Credit: Assessment will be based on assignments (20%) and a test (20%) and final exam (60%).

For Advice: Thomas Yee (Email: t.yee@auckland.ac.nz | extn: 88811), Yong Wang (Email: yong.wang@auckland.ac.nz | extn: 84700), Paul Murrell (Email: p.murrell@auckland.ac.nz | extn: 85392)

Taught: First Semester City, Second Semester City

Website: STATS 782 website

This course provides a more advanced treatment of statistical computing with R. 

The course is intended for students with a strong interest in computing or progressing on to a research career (or both).

Topics covered include: details of R syntax and data structures; numerical computation; object-oriented programming (S3 and S4); R packages; efficiency; scoping; and graphics.

The course will assume a background in statistical computing (e.g., STATS 220, STATS 380, or STATS 779) or a background in programming (e.g., COMPSCI programming courses or students in the data science programme). Students should have a reasonably strong maths background (e.g., to Stage 2).

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STATS 785 Topics in Statistical Data Management


Below description edited in year: 2018

Points: 15

Prereqs: 15 points from STATS 201, STATS 208, BIOSCI 209

Restrictions: STATS 301

Credit: Assessment: 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. The STATS 785 exam will consist of 10% from chapters 1-6, 15% from Non Linear regression modelling and/or Statistical Bootstrapping along with SAS Macro Language and 75% from Chapters 7-12. STATS 785 assignments to be handed in at class on due dates.

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 785 website

This course includes the material in STATS 301. 
It is intended for postgraduate students who have not already passed STATS 301. 

One of the key purposes of STATS 785 is to introduce you to the SAS software for the purposes of statistical inference, programming and modeling. SAS is a major commercial statistics package that is used at about 40,000 sites worldwide, and by 4 million users. We will use SAS as a programming language, and some more advanced features of SAS programming.

STATS 785 is designed to be a practical course in the use of SAS in industry, such as, Market Research, Finance and Medicine. 

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 Modelling techniques include linear modelling, multivariate ANOVA, tables of counts.

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