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2176

STATS 310

: Introduction to Statistical Inference
2023 Semester One (1233)
Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250 or equivalent
Restriction: STATS 732
2177

STATS 310

: Introduction to Statistical Inference
2022 Semester One (1223)
Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250 or equivalent
Restriction: STATS 732
2178

STATS 310

: Introduction to Statistical Inference
2021 Semester One (1213)
Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250 or equivalent
Restriction: STATS 732
2179

STATS 310

: Introduction to Statistical Inference
2020 Semester One (1203)
Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250 or equivalent
Restriction: STATS 732
2180

STATS 313

: Advanced Topics in Probability
2022 Semester Two (1225)
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.
Subject: Statistics
Prerequisite: STATS 225
Restriction: STATS 710
2181

STATS 320

: Applied Stochastic Modelling
2023 Semester One (1233)
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, 208, 220, or ENGSCI 314
2182

STATS 320

: Applied Stochastic Modelling
2022 Semester One (1223)
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
2183

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
2184

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
2185

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
2186

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
2187

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
2188

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
2189

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
2190

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
2191

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
2192

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
2193

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
2194

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
2195

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
2196

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
2197

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
2198

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
2199

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
2200

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