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Showing 25 course outlines from 3703 matches
2776
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
2777
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
2778
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
2779
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
2780
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
2781
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
2782
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
2783
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
2784
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
2785
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
2786
STATS 331
: Introduction to Bayesian Statistics2021 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.
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2787
STATS 331
: Introduction to Bayesian Statistics2020 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.
Prerequisite: 15 points from STATS 201, 207, 208, BIOSCI 209
2788
STATS 369
: Data Science Practice2024 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.
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
Restriction: STATS 765
2789
STATS 369
: Data Science Practice2023 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.
Prerequisite: STATS 220 and STATS 210 or 225 and 15 points from ECON 221, STATS 201, 208, or ENGSCI 314
Restriction: STATS 765
Restriction: STATS 765
2790
STATS 369
: Data Science Practice2022 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.
Prerequisite: STATS 220, and STATS 210 or 225, and 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208
Restriction: STATS 765
Restriction: STATS 765
2791
STATS 369
: Data Science Practice2021 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.
Prerequisite: 15 points from BIOSCI 209, ECON 211, STATS 201, 207, 208, and STATS 210 or 225
Restriction: STATS 765
Restriction: STATS 765
2792
STATS 369
: Data Science Practice2020 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.
Prerequisite: STATS 220, 201 or 208, 210 or 225
Restriction: STATS 765
Restriction: STATS 765
2793
STATS 370
: Financial Mathematics2024 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.
Prerequisite: 15 points at Stage II in Mathematics and 15 points at Stage II in Statistics
Restriction: STATS 722
Restriction: STATS 722
2794
STATS 370
: Financial Mathematics2023 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.
Prerequisite: 15 points at Stage II in Mathematics and 15 points at Stage II in Statistics
Restriction: STATS 722
Restriction: STATS 722
2795
STATS 370
: Financial Mathematics2022 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.
Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209; 15 points at Stage II in Mathematics
Restriction: STATS 722
Restriction: STATS 722
2796
STATS 370
: Financial Mathematics2020 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.
Prerequisite: 15 points at Stage II in Statistics or BIOSCI 209; 15 points at Stage II in Mathematics
Restriction: STATS 722
Restriction: STATS 722
2797
STATS 380
: Statistical Computing2024 Semester Two (1245)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
2798
STATS 380
: Statistical Computing2024 Semester One (1243)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
2799
STATS 380
: Statistical Computing2023 Semester Two (1235)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
2800
STATS 380
: Statistical Computing2023 Semester One (1233)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
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