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3301

STATS 225

: Probability: Theory and Applications
2021 Semester One (1213)
Covers the fundamentals of probability through theory, methods, and applications. Topics should include the classical limit theorems of probability and statistics known as the laws of large numbers and central limit theorem, conditional expectation as a random variable, the use of generating function techniques, and key properties of some fundamental stochastic models such as random walks, branching processes and Poisson point processes.
Subject: Statistics
Prerequisite: B+ or higher in ENGGEN 150 or ENGSCI 111 or STATS 125, or a B+ or higher in MATHS 120 and 130 Corequisite: 15 points from ENGSCI 211, MATHS 208, 250
3302

STATS 225

: Probability: Theory and Applications
2020 Semester One (1203)
Covers the fundamentals of probability through theory, methods, and applications. Topics should include the classical limit theorems of probability and statistics known as the laws of large numbers and central limit theorem, conditional expectation as a random variable, the use of generating function techniques, and key properties of some fundamental stochastic models such as random walks, branching processes and Poisson point processes.
Subject: Statistics
Prerequisite: 15 points from ENGGEN 150, ENGSCI 111, STATS 125 with a B+ or higher, or MATHS 120 and 130 with a B+ or higher Corequisite: 15 points from MATHS 250, ENGSCI 211 or equivalent
3303

STATS 240

: Design and Structured Data
2025 Semester Two (1255)
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.
Subject: Statistics
Prerequisite: STATS 101 or 108
Restriction: STATS 340
3304

STATS 240

: Design and Structured Data
2024 Semester Two (1245)
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.
Subject: Statistics
Prerequisite: STATS 101 or 108
Restriction: STATS 340
3305

STATS 240

: Design and Structured Data
2023 Semester Two (1235)
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.
Subject: Statistics
Prerequisite: STATS 101 or 108
Restriction: STATS 340
3306

STATS 240

: Design and Structured Data
2022 Semester Two (1225)
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.
Subject: Statistics
Prerequisite: STATS 101 or 108
Restriction: STATS 340
3307

STATS 240

: Design and Structured Data
2021 Semester Two (1215)
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.
Subject: Statistics
Prerequisite: STATS 101 or 108
Restriction: STATS 340
3308

STATS 240

: Design and Structured Data
2020 Semester Two (1205)
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.
Subject: Statistics
Prerequisite: STATS 101 or 108
Restriction: STATS 340
3309

STATS 255

: Optimisation and Data-driven Decision Making
2025 Semester One (1253)
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.
Subject: Statistics
Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208
Restriction: ENGSCI 255
3310

STATS 255

: Optimisation and Data-driven Decision Making
2024 Semester One (1243)
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.
Subject: Statistics
Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208
Restriction: ENGSCI 255
3311

STATS 255

: Optimisation and Data-driven Decision Making
2023 Semester One (1233)
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.
Subject: Statistics
Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208
Restriction: ENGSCI 255
3312

STATS 255

: Optimisation and Data-driven Decision Making
2022 Semester One (1223)
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.
Subject: Statistics
Prerequisite: ENGSCI 211 or STATS 201 or 208, or a B+ or higher in either MATHS 108 or 120 or 130 or 150 or 153 or 162 or 199 or STATS 101 or 108, or a concurrent enrolment in either ENGSCI 211 or STATS 201 or 208
Restriction: ENGSCI 255
3313

STATS 255

: Optimisation and Data-driven Decision Making
2021 Semester One (1213)
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.
Subject: Statistics
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
3314

STATS 255

: Optimisation and Data-driven Decision Making
2020 Semester Two (1205)
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.
Subject: Statistics
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
3315

STATS 255

: Optimisation and Data-driven Decision Making
2020 Semester One (1203)
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.
Subject: Statistics
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
3316

STATS 301

: Statistical Programming and Modelling using SAS
2021 Semester Two (1215)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
Restriction: STATS 785
3317

STATS 301

: Statistical Programming and Modelling using SAS
2021 Summer School (1210)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
Restriction: STATS 785
3318

STATS 301

: Statistical Programming and Modelling using SAS
2020 Semester Two (1205)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
Restriction: STATS 785
3319

STATS 301

: Statistical Programming and Modelling using SAS
2020 Summer School (1200)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
Restriction: STATS 785
3320

STATS 302

: Applied Multivariate Analysis
2025 Semester One (1253)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
Restriction: STATS 767
3321

STATS 302

: Applied Multivariate Analysis
2024 Semester One (1243)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
Restriction: STATS 767
3322

STATS 302

: Applied Multivariate Analysis
2023 Semester One (1233)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
Restriction: STATS 767
3323

STATS 302

: Applied Multivariate Analysis
2022 Semester One (1223)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
Restriction: STATS 767
3324

STATS 302

: Applied Multivariate Analysis
2021 Semester One (1213)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
Restriction: STATS 767
3325

STATS 302

: Applied Multivariate Analysis
2020 Semester One (1203)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
Restriction: STATS 767