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Showing 25 course outlines from 4482 matches

3451

STATS 727

: Foundations of Applied Time Series Analysis
2020 Semester One (1203)
Fundamentals of applied time series analysis. Topics include: components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas are presented.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, ECON 221, STATS 201, 207, 208
Restriction: STATS 326
3452

STATS 730

: Statistical Inference
2025 Semester Two (1255)
Fundamental topics in estimation and statistical inference. Advanced topics in modelling including regression with dependent data, survival analysis, methods to handle missing data. Advanced topics in current statistical practice researched by students. Students will undertake and present individual research projects on assigned topics, consisting in a literature search and a computational application to a data analysis task.
Subject: Statistics
Prerequisite: STATS 310 or 732
3453

STATS 730

: Statistical Inference
2024 Semester Two (1245)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
3454

STATS 730

: Statistical Inference
2023 Semester Two (1235)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
3455

STATS 730

: Statistical Inference
2022 Semester Two (1225)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
3456

STATS 730

: Statistical Inference
2021 Semester Two (1215)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
3457

STATS 730

: Statistical Inference
2020 Semester Two (1205)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Subject: Statistics
Prerequisite: STATS 310 or 732
3458

STATS 731

: Bayesian Inference
2025 Semester Two (1255)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Subject: Statistics
Prerequisite: STATS 331 and 15 points from STATS 210, 225
3459

STATS 731

: Bayesian Inference
2024 Semester Two (1245)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Subject: Statistics
Prerequisite: STATS 331 and 15 points from STATS 210, 225
3460

STATS 731

: Bayesian Inference
2023 Semester Two (1235)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Subject: Statistics
Prerequisite: STATS 331 and 15 points from STATS 210, 225
3461

STATS 731

: Bayesian Inference
2022 Semester Two (1225)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Subject: Statistics
Prerequisite: STATS 210 or 225
3462

STATS 731

: Bayesian Inference
2021 Semester Two (1215)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Subject: Statistics
Prerequisite: STATS 210 or 225
3463

STATS 731

: Bayesian Inference
2020 Semester One (1203)
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Subject: Statistics
Prerequisite: STATS 210 or 225
3464

STATS 732

: Foundations of Statistical Inference
2025 Semester One (1253)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
3465

STATS 732

: Foundations of Statistical Inference
2024 Semester One (1243)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
3466

STATS 732

: Foundations of Statistical Inference
2023 Semester One (1233)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
3467

STATS 732

: Foundations of Statistical Inference
2022 Semester One (1223)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
3468

STATS 732

: Foundations of Statistical Inference
2021 Semester One (1213)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
3469

STATS 732

: Foundations of Statistical Inference
2020 Semester One (1203)
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
Subject: Statistics
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
3470

STATS 740

: Sample Surveys
2024 Semester One (1243)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Subject: Statistics
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
3471

STATS 740

: Sample Surveys
2022 Semester Two (1225)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Subject: Statistics
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
3472

STATS 740

: Sample Surveys
2021 Semester Two (1215)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Subject: Statistics
Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics
3473

STATS 740

: Sample Surveys
2020 Semester Two (1205)
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non-sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Subject: Statistics
Prerequisite: 15 points from STATS 340, 741 and 15 points from STATS 310, 732
3474

STATS 747

: Statistical Methods in Marketing
2021 Semester Two (1215)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
Subject: Statistics
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
3475

STATS 747

: Statistical Methods in Marketing
2020 Semester Two (1205)
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
Subject: Statistics
No pre-requisites or restrictions