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Showing 25 course outlines from 4702 matches
3426
STATS 325
: Stochastic Processes2022 Semester Two (1225)
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
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
Restriction: STATS 721
3427
STATS 325
: Stochastic Processes2021 Semester Two (1215)
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
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
Restriction: STATS 721
3428
STATS 325
: Stochastic Processes2020 Semester Two (1205)
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
Restriction: STATS 721
Restriction: STATS 721
3429
STATS 326
: Applied Time Series Analysis2025 Semester One (1253)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Prerequisite: 15 points from ECON 211, ENGSCI 314, STATS 201, 208
Restriction: STATS 727
Restriction: STATS 727
3430
STATS 326
: Applied Time Series Analysis2024 Semester One (1243)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Prerequisite: 15 points from ECON 211, ENGSCI 314, STATS 201, 208
Restriction: STATS 727
Restriction: STATS 727
3431
STATS 326
: Applied Time Series Analysis2023 Semester One (1233)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Prerequisite: 15 points from ECON 211, ENGSCI 314, STATS 201, 208
Restriction: STATS 727
Restriction: STATS 727
3432
STATS 326
: Applied Time Series Analysis2022 Semester One (1223)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208
Restriction: STATS 727
Restriction: STATS 727
3433
STATS 326
: Applied Time Series Analysis2021 Semester One (1213)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208
Restriction: STATS 727
Restriction: STATS 727
3434
STATS 326
: Applied Time Series Analysis2021 Summer School (1210)
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208
Restriction: STATS 727
Restriction: STATS 727
3435
STATS 326
: Applied Time Series Analysis2020 Semester One (1203)
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
Restriction: STATS 727
Restriction: STATS 727
3436
STATS 326
: Applied Time Series Analysis2020 Summer School (1200)
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
Restriction: STATS 727
Restriction: STATS 727
3437
STATS 330
: Statistical Modelling2025 Semester Two (1255)
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: ENGSCI 314 or STATS 201 or 208
3438
STATS 330
: Statistical Modelling2025 Semester One (1253)
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: ENGSCI 314 or STATS 201 or 208
3439
STATS 330
: Statistical Modelling2025 Summer School (1250)
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: ENGSCI 314 or STATS 201 or 208
3440
STATS 330
: Statistical Modelling2024 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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
3441
STATS 330
: Statistical Modelling2024 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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
3442
STATS 330
: Statistical Modelling2024 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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
3443
STATS 330
: Statistical Modelling2023 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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
3444
STATS 330
: Statistical Modelling2023 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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
3445
STATS 330
: Statistical Modelling2023 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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
3446
STATS 330
: Statistical Modelling2022 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.
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
3447
STATS 330
: Statistical Modelling2022 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.
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
3448
STATS 330
: Statistical Modelling2022 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.
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
3449
STATS 330
: Statistical Modelling2021 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.
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
3450
STATS 330
: Statistical Modelling2021 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.
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
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