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Showing 25 course outlines from 3705 matches

2776

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
2777

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
2778

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
2779

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
2780

STATS 330

: Statistical Modelling
2020 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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2781

STATS 330

: Statistical Modelling
2020 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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2782

STATS 331

: Introduction to Bayesian Statistics
2024 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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2783

STATS 331

: Introduction to Bayesian Statistics
2023 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.
Subject: Statistics
Prerequisite: ENGSCI 314 or STATS 201 or 208
2784

STATS 331

: Introduction to Bayesian Statistics
2022 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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2785

STATS 331

: Introduction to Bayesian Statistics
2021 Semester Two (1215)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2786

STATS 331

: Introduction to Bayesian Statistics
2020 Semester Two (1205)
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.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2787

STATS 369

: Data Science Practice
2024 Semester Two (1245)
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Subject: Statistics
Prerequisite: STATS 220 and STATS 210 or 225 and 15 points from ECON 221, STATS 201, 208, or ENGSCI 233 and 263
Restriction: STATS 765
2788

STATS 369

: Data Science Practice
2023 Semester Two (1235)
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Subject: Statistics
Prerequisite: STATS 220 and STATS 210 or 225 and 15 points from ECON 221, STATS 201, 208, or ENGSCI 314
Restriction: STATS 765
2789

STATS 369

: Data Science Practice
2022 Semester Two (1225)
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Subject: Statistics
Prerequisite: STATS 220, and STATS 210 or 225, and 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208
Restriction: STATS 765
2790

STATS 369

: Data Science Practice
2021 Semester Two (1215)
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208, and STATS 210 or 225
Restriction: STATS 765
2791

STATS 369

: Data Science Practice
2020 Semester Two (1205)
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Subject: Statistics
Prerequisite: STATS 220, 201 or 208, 210 or 225
Restriction: STATS 765
2792

STATS 370

: Financial Mathematics
2024 Semester Two (1245)
Mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Subject: Statistics
Prerequisite: 15 points at Stage II in Mathematics and 15 points at Stage II in Statistics
Restriction: STATS 722
2793

STATS 370

: Financial Mathematics
2023 Semester Two (1235)
Mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Subject: Statistics
Prerequisite: 15 points at Stage II in Mathematics and 15 points at Stage II in Statistics
Restriction: STATS 722
2794

STATS 370

: Financial Mathematics
2022 Semester Two (1225)
Mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Subject: Statistics
Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209; 15 points at Stage II in Mathematics
Restriction: STATS 722
2795

STATS 370

: Financial Mathematics
2020 Semester Two (1205)
Mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Subject: Statistics
Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209; 15 points at Stage II in Mathematics
Restriction: STATS 722
2796

STATS 380

: Statistical Computing
2024 Semester Two (1245)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Subject: Statistics
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
2797

STATS 380

: Statistical Computing
2024 Semester One (1243)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Subject: Statistics
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
2798

STATS 380

: Statistical Computing
2023 Semester Two (1235)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Subject: Statistics
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
2799

STATS 380

: Statistical Computing
2023 Semester One (1233)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Subject: Statistics
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
2800

STATS 380

: Statistical Computing
2022 Semester Two (1225)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Subject: Statistics
Prerequisite: 15 points from STATS 201, 207, 208, 220, BIOSCI 209