| source University of Auckland (X) |
level |
department Statistics (X) |
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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. Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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. Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs. Design, implementation and analysis of surveys including such topics as questionnaire design, sampling design and the analysis of data from stratified, multistage and cluster sampling.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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. Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs. Design, implementation and analysis of surveys including such topics as questionnaire design, sampling design and the analysis of data from stratified, multistage and cluster sampling. Design and implementation issues for statistically designed experiments and the analysis of data from designs such as incomplete block, Latin square, split plot, factorial and fractional designs.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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. Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs. Design, implementation and analysis of surveys including such topics as questionnaire design, sampling design and the analysis of data from stratified, multistage and cluster sampling. Design and implementation issues for statistically designed experiments and the analysis of data from designs such as incomplete block, Latin square, split plot, factorial and fractional designs. 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.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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. Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs. Design, implementation and analysis of surveys including such topics as questionnaire design, sampling design and the analysis of data from stratified, multistage and cluster sampling. Design and implementation issues for statistically designed experiments and the analysis of data from designs such as incomplete block, Latin square, split plot, factorial and fractional designs. 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. Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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. Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs. Design, implementation and analysis of surveys including such topics as questionnaire design, sampling design and the analysis of data from stratified, multistage and cluster sampling. Design and implementation issues for statistically designed experiments and the analysis of data from designs such as incomplete block, Latin square, split plot, factorial and fractional designs. 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. Statistical programming using the R computing environment. Data structures, numerical computing and graphics. Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas.
Score: 9.11431 Details | Listing | Web page
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test. The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce. Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk. Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news. A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. A practical course in the statistical analysis of data, with hands on experience in research design and execution. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection. The primary coursework assessment will be a group project. A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered. Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing. This course is a prerequisite for the BSc(Hons) and Master's degree in Statistics. Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes. Emphasises the relationship between business and industrial applications and their associated operations research models. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models and simulation will be considered. Introduction to the SAS statistical software with emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods. Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods. Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models. Introduction to stochastic modelling, with an emphasis on queues and models used in finance. Behaviour of Poisson processes, queues and continuous time Markov chains will be investigated using theory and simulation. Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks. Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas. 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. 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. Design, implementation and analysis of surveys including questionnaire design, sampling design and the analysis of data from stratified, cluster and multistage sampling. Design and implementation issues for scientific experiments including blocking, replication and randomisation and the analysis of data from designs such as complete block, balanced incomplete block, Latin square, split plot, factorial and fractional designs. Design, implementation and analysis of surveys including such topics as questionnaire design, sampling design and the analysis of data from stratified, multistage and cluster sampling. Design and implementation issues for statistically designed experiments and the analysis of data from designs such as incomplete block, Latin square, split plot, factorial and fractional designs. 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. Statistical programming using the R computing environment. Data structures, numerical computing and graphics. Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas. Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem.
Score: 9.11431 Details | Listing | Web page