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Showing 25 course outlines from 3702 matches

2751

STATS 320

: Applied Stochastic Modelling
2021 Semester One (1213)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 125, 210, 225 and 15 points from STATS 201, 207, 208, 220, BIOSCI 209
2752

STATS 320

: Applied Stochastic Modelling
2020 Semester One (1203)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 125, 210, 225 and 15 points from STATS 201, 207, 208, 220, BIOSCI 209
2753

STATS 325

: Stochastic Processes
2024 Semester Two (1245)
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Subject: Statistics
Prerequisite: B+ or higher in STATS 125 or B or higher in ENGSCI 314 or STATS 210 or 225 or 320, and 15 points from ENGSCI 211, MATHS 208, 250
Restriction: STATS 721
2754

STATS 325

: Stochastic Processes
2023 Semester Two (1235)
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Subject: Statistics
Prerequisite: B+ or higher in STATS 125 or B or higher in ENGSCI 314 or STATS 210 or 225 or 320, and 15 points from ENGSCI 211, MATHS 208, 250
Restriction: STATS 721
2755

STATS 325

: Stochastic Processes
2022 Semester Two (1225)
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Subject: Statistics
Prerequisite: B+ or higher in STATS 125 or B or higher in STATS 210 or 225 or 320, and 15 points from ENGSCI 211, MATHS 208, 250
Restriction: STATS 721
2756

STATS 325

: Stochastic Processes
2021 Semester Two (1215)
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Subject: Statistics
Prerequisite: B+ or higher in STATS 125 or B or higher in STATS 210 or 225 or 320, and 15 points from ENGSCI 211, MATHS 208, 250
Restriction: STATS 721
2757

STATS 325

: Stochastic Processes
2020 Semester Two (1205)
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Subject: Statistics
Prerequisite: 15 points from STATS 125, 210, 320, with at least a B pass, 15 points from MATHS 208, 250, 253
Restriction: STATS 721
2758

STATS 326

: Applied Time Series Analysis
2024 Semester One (1243)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Subject: Statistics
Prerequisite: 15 points from ECON 211, ENGSCI 314, STATS 201, 208
Restriction: STATS 727
2759

STATS 326

: Applied Time Series Analysis
2023 Semester One (1233)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Subject: Statistics
Prerequisite: 15 points from ECON 211, ENGSCI 314, STATS 201, 208
Restriction: STATS 727
2760

STATS 326

: Applied Time Series Analysis
2022 Semester One (1223)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208
Restriction: STATS 727
2761

STATS 326

: Applied Time Series Analysis
2021 Semester One (1213)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208
Restriction: STATS 727
2762

STATS 326

: Applied Time Series Analysis
2021 Summer School (1210)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208
Restriction: STATS 727
2763

STATS 326

: Applied Time Series Analysis
2020 Semester One (1203)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 208, BIOSCI 209, ECON 221
Restriction: STATS 727
2764

STATS 326

: Applied Time Series Analysis
2020 Summer School (1200)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 208, BIOSCI 209, ECON 221
Restriction: STATS 727
2765

STATS 330

: Statistical Modelling
2024 Semester Two (1245)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2766

STATS 330

: Statistical Modelling
2024 Semester One (1243)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2767

STATS 330

: Statistical Modelling
2024 Summer School (1240)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2768

STATS 330

: Statistical Modelling
2023 Semester Two (1235)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2769

STATS 330

: Statistical Modelling
2023 Semester One (1233)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2770

STATS 330

: Statistical Modelling
2023 Summer School (1230)
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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2771

STATS 330

: Statistical Modelling
2022 Semester Two (1225)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2772

STATS 330

: Statistical Modelling
2022 Semester One (1223)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2773

STATS 330

: Statistical Modelling
2022 Summer School (1220)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2774

STATS 330

: Statistical Modelling
2021 Semester Two (1215)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2775

STATS 330

: Statistical Modelling
2021 Semester One (1213)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209