Search Course Outline
Showing 25 course outlines from 4482 matches
3351
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
3352
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
3353
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
3354
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
3355
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
3356
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
3357
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
3358
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
3359
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
3360
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
3361
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
3362
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
3363
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
3364
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
3365
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
3366
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
3367
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
3368
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
3369
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
3370
STATS 330
: Statistical Modelling2020 Semester Two (1205)
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
3371
STATS 330
: Statistical Modelling2020 Semester One (1203)
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
3372
STATS 331
: Introduction to Bayesian Statistics2025 Semester Two (1255)
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 ENGSCI 263, STATS 201, 208 and 15 points from ENGSCI 111, ENGGEN 150, STATS 125
3373
STATS 331
: Introduction to Bayesian Statistics2024 Semester Two (1245)
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: ENGSCI 314 or STATS 201 or 208
3374
STATS 331
: Introduction to Bayesian Statistics2023 Semester Two (1235)
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: ENGSCI 314 or STATS 201 or 208
3375
STATS 331
: Introduction to Bayesian Statistics2022 Semester Two (1225)
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
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