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Study online with Swinburne University of Technology through Open Universities Australia. Explore all degrees, subjects and short courses now.

The Graduate Certificate of Applied Statistics is designed for graduates in any discipline, especially for humanities, social sciences, health sciences, information systems and business graduates who have a professional interest in the use of statistics. It is also applicable to other graduates who have a need to use statistics in their work but have not had sufficient experience with applied statistics. It concentrates on practical skills and enables participants to broaden their theoretical and practical knowledge in the basic areas of statistics.

Learn quantitative skills in exploratory data analysis, estimation and hypothesis testing, factor and regression analysis, discriminant analysis and forecasting. Gain practical experience using statistical software packages such as SPSS and SAS

Graduate Certificate of Applied Statistics

Research Design

Basic Statistical Computing Using R

Statistical Practice 1

This subject introduces R, one of the popular open source statistical programming languages commonly used in applied statistics and data science disciplines. Students will learn key programming principles of R and develop competence in programming in R which are essential for a statistician or data scientist to perform different types of statistical analyses.

Upskill and package up your computer and statistical knowledge. Form questions. Use SPSS software to select, modify, merge and apply data visualisation techniques. Investigate missing values and manage complex data files. 

This subject teaches students to identify some of the methodologies used in survey research. It includes an overview of the procedures used in survey research, a descriptive approach to methods including questionnaire design, data collection, and an introduction to survey sampling. It covers ethics and laws in conducting research. It also covers topics related to incidence and prevalence, standardisation, different types of study designs including cohort and case-control studies, randomised control trials and clinical trials in health statistics and elsewhere. Case studies will be drawn from areas such as sociology, market research, psychology, medical sciences and other areas as appropriate.

Survey research methods and get critical about design. Describe approaches and observe techniques. Sample data collection methods. Scale and graph procedures and figure out incidence and prevalence. Draw on case studies and assess and present data

This subject extends the ideas developed in Statistical Practice 1 to include more advanced analyses, broaden the range of applications students are familiar with so that they will be able to carry out independent statistical investigations, and develop an awareness of the assumptions and limitations involved in the generalisation of results of such investigations.

Practice and advance your statistical knowledge and extend ideas. Produce an independent investigation. Get analytical and compare groups using parametric and non-parametric methods. Stop generalising, question assumptions and identify data limits. 

Statistical Practice 2

This subject develops students’ understanding of a range of statistical methods along with their assumptions and limitations of their application. It enables students to develop the capacity to carry out independent statistical analysis of data using a standard statistical software package and also aims to develop students’ abilities in effectively communicating the outcomes of statistical investigations. In addition, it will provide a foundation and motivation for exposure to statistical ideas subsequent to the Graduate Certificate (GC) degree.

Develop a number of statistical data techniques; assess theories and survey limitations. Increase analysis skills and translate investigation outcomes. Recognise statistical inference and sampling distributions. Transcribe interpretative summaries. 

This subject will introduce students to fundamental statistical areas of research design and linear models. It examines how multiple regression and Analysis of Variance (ANOVA) can be used to analyse experimental and observational research using a variety of research designs.
As the subject builds on the skills developed in STA10003 Foundations of Statistics, this prerequisite is strictly enforced.

Line up and calculate statistical modelling for a range of research designs. Examine how analysis of variance (ANOVA) and multiple regression methods are used to assess experimental and observational research data. Interpret results and write reports.

Analysis of Variance and Regression

Swinburne University of Technology Statistics Courses & Degrees | Open Universities Australia

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