Courses - Faculty of Science


Statistics

Stage I

STATS 100
15 Points

Functioning in Statistics

A first exposure to statistics that builds data handling skills and develops conceptual thinking through active participation in problems using real data, computer simulations and group work. STATS 100 makes full use of appropriate technology and prepares students for further study in Statistics. Restriction: STATS 100 may not be taken with, or after passing, 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

STATS 101
15 Points

STATS 101G
15 Points

Introduction to Statistics

Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. 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 and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test.

Restriction: STATS 102, 107, 108, 191

STATS 108
15 Points

Statistics for Commerce

The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce.

Restriction: STATS 101, 102, 107, 191

STATS 125
15 Points

Probability and its Applications

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.

Corequisite: MATHS 108 or 110 or 120 or 130

Restriction: STATS 210

STATS 150
15 Points

STATS 150G
15 Points

Lies, Damned Lies, and Statistics

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. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news.

Stage II

STATS 201
15 Points

Data Analysis

A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection.

Prerequisite: 15 points from STATS 101-108, 191

Restriction: STATS 207, 208, BIOSCI 209

STATS 208
15 Points

Data Analysis for Commerce

A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered.

Prerequisite: 15 points from STATS 101-108, 191

Restriction: STATS 201, 207, BIOSCI 209

STATS 210
15 Points

Statistical Theory

Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and masters degree in statistics.

Prerequisite: 15 points from ENGSCI 111, ENGGEN 150, STATS 125

Corequisite: 15 points from MATHS 208, 250, ENGSCI 211 or equivalent

STATS 220
15 Points

Data Technologies

Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.

Prerequisite: 15 points at Stage I in Computer Science or Statistics

STATS 225
15 Points

Mathematical Statistics

Multivariate probability and distributions, transformations, expectation, moment generating functions, likelihood and estimation, hypothesis testing.

Prerequisite: B+ in STATS 125 or ENGSCI 111 or ENGGEN 150

Corequisite: 15 points from MATHS 250, ENGSCI 211 or equivalent

STATS 240
15 Points

Design and Structured Data

An introduction to research study design and the analysis of structured data. Blocking, randomisation, and replication in designed experiments. Clusters, stratification, and weighting in samples. Other examples of structured data.

Prerequisite: STATS 101 or 108

Restriction: STATS 340

STATS 255
15 Points

Optimisation and Data-driven Decision Making

Explores methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualisation will be considered.

Prerequisite: 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

Restriction: ENGSCI 255

STATS 290
15 Points

Topics in Statistics

Prerequisite: 15 points from ENGSCI 111, ENGGEN 150, STATS 125

Restriction: STATS 210, 225

Stage III

STATS 301
15 Points

Statistical Programming and Modelling using SAS

Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods.

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

STATS 302
15 Points

Applied Multivariate Analysis

Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

STATS 310
15 Points

Introduction to Statistical Inference

Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.

Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250 or equivalent

STATS 313
15 Points

Advanced Topics in Probability

Characterisations of and relations between different kinds of random objects including random functions, random paths and random trees. Modes of convergence; the Law of Large Numbers and Central Limit Theorem.

Prerequisite: STATS 225

Restriction: STATS 710

STATS 320
15 Points

Applied Stochastic Modelling

Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation.

Prerequisite: 15 points from STATS 125, 210, 225 and 15 points from STATS 201, 207, 208, 220, BIOSCI 209

STATS 325
15 Points

Stochastic Processes

Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.

Prerequisite: 15 points from STATS 125, 210, 320, with at least a B pass, 15 points from MATHS 208, 250, 253

STATS 326
15 Points

Applied Time Series Analysis

Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.

Prerequisite: 15 points from STATS 201, 208, BIOSCI 209, ECON 221

STATS 330
15 Points

Statistical Modelling

Application of the generalised linear model and extensions to fit data arising from a range of sources including multiple regression models, logistic regression models, and log-linear models. The graphical exploration of data.

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

STATS 331
15 Points

Introduction to Bayesian Statistics

Introduces Bayesian data analysis using the WinBUGS software package and R. Topics include the Bayesian paradigm, hypothesis testing, point and interval estimates, graphical models, simulation and Bayesian inference, diagnosing MCMC, model checking and selection, ANOVA, regression, GLMs, hierarchical models and time series. Classical and Bayesian methods and interpretations are compared.

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

STATS 340
15 Points

Design and Analysis of Surveys and Experiments

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.

Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209

STATS 369
15 Points

Data Science Practice

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

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

STATS 370
15 Points

Financial Mathematics

Mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; 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.

Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209; 15 points at Stage II in Mathematics

STATS 380
15 Points

Statistical Computing

Statistical programming using the R computing environment. Data structures, numerical computing and graphics.

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

STATS 399
15 Points

Capstone: Statistics in Action

Provides opportunities to integrate statistical knowledge and collaborate with others through completion of a group-based project.

Prerequisite: 30 points at Stage III in Statistics

Postgraduate 700 Level Courses

STATS 701
15 Points

Advanced SAS Programming

A continuation of STATS 301, with more in-depth coverage of programming in the SAS language. Topics covered will include advanced use of the SAS language, advanced data step programming, macros, input and output, connectivity to other software platforms, SAS SQL.

Prerequisite: STATS 301

STATS 702
15 Points

Special Topic in Statistics 2

STATS 703
15 Points

Special Topic in Statistics 1

STATS 705
15 Points

Topics in Official Statistics

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.

STATS 707
15 Points

Computational Introduction to Statistics

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

Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162

Restriction: STATS 201, 208, 210, 225

STATS 708
15 Points

Topics in Statistical Education

Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas.

STATS 710
15 Points

Probability Theory

Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem.

Prerequisite: STATS 310, 320 or 325

STATS 720
15 Points

Stochastic Processes

Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.

Prerequisite: STATS 320 or 325

STATS 721
15 Points

Special Topic in Applied Probability

STATS 722
15 Points

Financial Mathematics

STATS 723
15 Points

Stochastic Methods in Finance

Contingent claims theory in discrete and continuous time. Risk-neutral option pricing, Cox-Ross-Rubinstein and Black-Scholes models, stochastic calculus, hedging and risk management.

Prerequisite: STATS 210 or 225

STATS 724
15 Points

Operations Research

Continuous-time Markov processes; optimisation for jump Markov processes; Markov decision processes; queueing theory and stochastic networks.

Prerequisite: 15 points from STATS 320, 325, 720 with at least B+

STATS 725
15 Points

Topics in Operations Research

STATS 726
15 Points

Time Series

Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.

STATS 727
15 Points

Special Topic in Time Series

STATS 730
15 Points

Statistical Inference

Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.

Prerequisite: STATS 310 or 732

STATS 731
15 Points

Bayesian Inference

A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.

Prerequisite: STATS 210 or 225

STATS 732
15 Points

Foundations of Statistical Inference

STATS 732 is an extended version of STATS 310 which covers the same material in greater depth. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.

Restriction: STATS 310

STATS 735
15 Points

Statistics in Ecology and Population Genetics

Concepts of population modelling and inference from ecological and genetic data. Topics covered include estimation of population size, spatial models, genetic structure and assignment. No previous knowledge of ecology or genetics is required. Recommended preparation: STATS 730

Prerequisite: STATS 310 or 732

STATS 737
15 Points

Modern Bayesian Methods

Concepts and tools underlying Bayesian methods in many modern areas of statistics. Advanced Markov-chain Monte Carlo, model evaluation using information criteria and Bayesian cross-validation, robustness, Bayesian non-parametrics. Applications may include hierarchical modelling, times-series, spatial data, Bayesian networks, genetics, approximate Bayesian computation for big data, artificial intelligence.

Prerequisite: STATS 731

STATS 740
15 Points

Sample Surveys

The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. 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. Concluding surveys: Analysis; Dissemination.

Prerequisite: 15 points from STATS 340, 741 and 15 points from STATS 310, 732

STATS 741
15 Points

Special Topic in Sampling

STATS 747
15 Points

Statistical Methods in Marketing

Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.

STATS 750
15 Points

Experimental Design

The design and analysis of data from experiments involving factorial and related designs and designs which have the property known as general balance (this includes most of the standard designs), and more general designs with blocking and replication. Response surface methodology. Sequential experimentation.

Prerequisite: 15 points from STATS 340, 351

STATS 751
15 Points

Special Topic in Experimental Design

STATS 760
15 Points

A Survey of Modern Applied Statistics

A survey of techniques from modern applied statistics. Topics covered will be linear, non-linear and generalised linear models, modern regression including CART and neural networks, mixed models, survival analysis, time series and spatial statistics.

Prerequisite: STATS 310, 330

STATS 761
15 Points

Mixed Models

Linear mixed effect models for the analysis of data from small experiments, particularly those cases where the data are unbalanced. Methods include restricted maximum likelihood for the estimation of variance components.

STATS 762
15 Points

Regression for Data Science

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. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.

Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209

Restriction: STATS 330

STATS 763
15 Points

Advanced Regression Methodology

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.

STATS 764
15 Points

Analysis of Failure Time Data

Topics in the theory and analysis of survival data. Survival data arises both in the Health Sciences and in industrial testing. Such data is often subject to censoring and truncated data. Both parametric and nonparametric methods, such as Kaplan-Meier estimates, will be covered. Other topics may include: proportional hazards regression, censored data and reliability.

Prerequisite: STATS 310

STATS 766
15 Points

Multivariate Analysis

A selection of topics from multivariate analysis, including: advanced methods of data display (eg, Correspondence and Canonical Correspondence Analysis, Biplots, and PREFMAP) and an introduction to classification methods (eg, various types of Discriminant Function Analysis).

Prerequisite: STATS 302 or 767

STATS 767
15 Points

Topics in Multivariate Analysis

STATS 768
15 Points

Longitudinal Data Analysis

Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.

STATS 769
15 Points

Advanced Data Science Practice

Databases, SQL, scripting, distributed computation, other data technologies.

STATS 770
15 Points

Introduction to Medical Statistics

An 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.

STATS 771
15 Points

Topics in Biostatistics 1

STATS 773
15 Points

Design and Analysis of Clinical Trials

The theory and practice of clinical trials, including: design issues, data management, common analysis methodologies, intention to treat, compliance, interim analyses and ethical considerations.

STATS 775
15 Points

Design of Ecological Experiments

Factorial designs, nested hierarchies and mixed models; variance components and expected mean squares; precision and power analysis; multivariate analysis in ecology; designs to detect environmental impact; resampling methods and permutation tests for complex designs.

STATS 776
15 Points

Topics in Environmental and Ecological Statistics

STATS 779
15 Points

Professional Skills for Statisticians

Statistical software, data management, data integrity, data transfer, file processing, symbolic manipulation, document design and presentation, oral presentation, professional ethics.

STATS 780
15 Points

Statistical Consulting

Students will learn about the practicalities of statistical consulting. Students will carry out a statistical consulting project, including the writing of a report, under the supervision of a member of the academic staff.

STATS 781
30 Points

STATS 781A
15 Points

STATS 781B
15 Points

Honours Project in Statistics

Restriction: STATS 789

To complete this course students must enrol in STATS 781 A and B, or STATS 781

STATS 782
15 Points

Statistical Computing

Professional skills, advanced statistical programming, numerical computation and graphics.

STATS 783
15 Points

Simulation and Monte Carlo Methods

A practical introduction to modern simulation and Monte Carlo techniques and their use to simulate real situations and to solve difficult statistical inferential problems whose mathematical analysis is intractable.

STATS 784
15 Points

Statistical Data Mining

Data cleaning, missing values, data warehouses, security, fraud detection, meta-analysis, and statistical techniques for data mining such as regression and decision trees, modern and semiparametric regression, neural networks, statistical approaches to the classification problem.

Prerequisite: 15 points from STATS 210, 225, and 15 points from STATS 330, 762

STATS 785
15 Points

Topics in Statistical Data Management

STATS 786
15 Points

Special Topic in Statistical Computing

STATS 787
15 Points

Data Visualisation

Effective visual presentations of data. Topics may include: how to present different types of data; human perception; graphics formats; statistical graphics in R; interactive graphics; visualising high-dimensional data; visualising large data. A background in statistical computing (eg, STATS 220, 380, 779) or programming will be assumed (eg, COMPSCI programming courses or students in the data science programme).

STATS 788
45 Points

STATS 788A
22.5 Points

STATS 788B
22.5 Points

Dissertation in Medical Statistics

To complete this course students must enrol in STATS 788 A and B, or STATS 788

STATS 790
30 Points

STATS 790A
15 Points

STATS 790B
15 Points

Masters Dissertation 1

Restriction: STATS 796

To complete this course students must enrol in STATS 790 A and B, or STATS 790

STATS 792A
22.5 Points

STATS 792B
22.5 Points

Dissertation in Statistics Education

To complete this course students must enrol in STATS 792 A and B

STATS 793
45 Points

STATS 793A
22.5 Points

STATS 793B
22.5 Points

Dissertation

To complete this course students must enrol in STATS 793 A and B, or STATS 793

STATS 798A
45 Points

STATS 798B
45 Points

Masters Thesis in Statistics

Prerequisite: 15 points from STATS 310, 732 and 15 points from STATS 330, 762, or approval of Head of Department

Restriction: STATS 790, 796

To complete this course students must enrol in STATS 798 A and B

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