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• Webinar: Can multiple disparate data sources complement each other? The case of researching economic disadvantage in Australia.

  • When
    17 Aug 2020
  •  
    1:00 PM - 2:00 PM (UTC+10:00)
  • Location
    via Zoom, times are in AEST

  Register

  The Statistical Society of Australia, the Australian Bureau of Statistics and the University of Melbourne are pleased to announce the following joined webinar:
  

  Can multiple disparate data sources complement each other?

  The case of researching economic disadvantage in Australia

  to be held on 17 August 2020 from 1:00PM -2:00PM.

  About this webinar

  The webinar is the first in a series of two with a data integration theme. Through the lens of researching economic disadvantage in Australia, Dr. Anders Holmberg and Dr. Rajeev Samarage will talk about data quality and methodology considerations that arise when a research project uses many fundamentally different and disparate data sources.

  Often there are good reasons in research to reuse already existing data. The obvious drawback of doing so is that the data collection and content is not ideal for the problem at hand. Nevertheless, cost efficiency, adequate methodology and the possibility to use multiple data sources are factors that may compensate and encourage such research designs. This webinar discusses a systematic way to assess the benefits and improve the quality when using multiple sources in a complex research problem. The need to explain quality in such situation has increased with the trend internationally to make more data safely available to a broad range of research uses. The presented framework has a step-wise approach for researchers to make trade-offs and choose the right mix of units and variables. It also illustrates where there are demands for more statistical research. This will be highlighted in the webinar.

  About the presenters

  Dr. Anders Holmberg is the Chief Methodologist at the Australian Bureau of Statistics. He has a background working with various types of data sources for official statistics in many different countries. This include design and collection of sample survey data, use of multiple administrative data sources for population censuses and combining surveys well as administrative data of people and businesses for statistical and research purposes.

  Dr. Rajeev Samarage is the Data and Analytics Lead at the Melbourne Institute: Applied Economic and Social Research within the University of Melbourne. He has a background in engineering with data analytics experience in developing medical imaging technology. This includes data visualisation and he is currently applying his skills to data analytics in the social sciences. He is also involved in data management practices and oversees the development of MIDL (the Melbourne Institute Data Lab), a secure research facility for conducting analyses of a range of data sources including survey and administrative data.

  To register

  This is a free event, but you will need to register. Click on the button on the left to save your place. After registering, you will receive a confirmation email containing information about joining the meeting. 

  If you have any questions, please contact Marie-Louise Rankin.

  Our Guarantee
  Our goal is to provide truly exceptional offerings and service, and we won't be happy until you are. If any programs, products or services of SSA do not fulfill our promise, we will make the situation right.

  Event Cancellation or Postponement
  SSA reserves exclusive right to modify, postpone/reschedule or cancel programs for any reason, including but not limited to emergency, inclement weather or other 'acts of God'. If there is an event cancellation, every attempt will be made to reschedule, and registration fees will be applied to the rescheduled event date. Any travel, lodging, or incidental expenses incurred related to a cancelled event cannot be refunded under any circumstances. 

  Consent to Use of Photographic Images
  Registration and attendance at, or participation in, SSA meetings and other activities constitutes an agreement by the registrant to the use and distribution of the registrant or attendees' image or voice in photographs, videotapes, electronic reproductions and audiotapes of such events and activities by SSA. 

  
  

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• External event: Introduction to R

  • Start
    19 Aug 2020
  •  
    9:30 AM (UTC+10:00)
  • End
    20 Aug 2020
  •  
    1:00 PM (UTC+10:00)

  Course Overview

  R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is open-source software, and RStudio, which is a versatile, user-friendly interface for using R. It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers.

  This course will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is self-paced and focused on developing practical skills.

  Course Requirements

  You will need a computer with course access (to install R before attending the course).

  Course 1:          Tuesday 11 & Wednesday 12 August, 2020 (two half-day sessions)

  Course 2:         Wednesday 19 & Thursday 20 August, 2020 (two half-day sessions)

  Duration: 9.30 am to 1.00 pm – Each day

  Location: Online

  For further details and bookings: 

  https://www.eventbrite.com.au/e/online-short-course-introduction-to-r-tickets-110931685658

  Stats Central provides study design and analysis support to all UNSW researchers, in collaborative and consultative roles, and conducts short courses and monthly seminars on topical issues. Please see below for our upcoming seminar and short courses. Please feel free to circulate this email to your colleagues, staff, researchers and students, who may be interested. Should you have any questions please don’t hesitate to contact us or visit our website statscentral.unsw.edu.au.

  **Due to the COVID-19 situation, we are offering online course with 20% discounted rates.**

  Note: The courses will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.

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• SA Branch webinar - A LEGENDARY way to do observational data analysis at scale!

  • When
    19 Aug 2020
  •  
    6:00 PM - 7:00 PM (UTC+09:30)
  • Location
    virtual Zoom meeting

  Branch Meeting - Wednesday, 19th August 2020

  The South Australian Branch of the Statistical Society would like to invite you to the August meeting of the 2020 program.

  Virtual Venue: Join the Zoom meeting using your PC or device https://unisa.zoom.us/j/93416975159?pwd=eWU4aWo5MjZTZ3YwajlRekd6ekhaZz09 
  Password: 718170
  
  or Join from dial-in phone line:  Dial: +61 2 8015 2088

  Meeting ID: 934 1697 5159
  

  
  

  Speaker: A/Prof Nicole Pratt, University of South Australia
  

  Topic: A LEGENDARY way to do observational data analysis at scale!
  

  Abstract

  Objectives: Evidence derived from existing healthcare data including administrative claims and electronic health records can fill evidence gaps in medicine, but is often criticized due to the potential for observational study bias, for example due to residual confounding. Other concerns include p-hacking and publication bias. Here we detail a set of principles embodying a new paradigm for observational research aimed at addressing these concerns, and describe a generic implementation of these principles.

  Materials and Methods: The Observational Health Data Sciences and Informatics (OHDSI) collaborative launced the Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative, aiming to generate evidence on the effects of medical interventions using observational healthcare databases. We define ten principles of LEGEND, prescribing the generation and dissemination of evidence on many research questions at once, for example comparing all treatments for a disease for many outcomes, thus preventing publication bias. These questions are answered using a prespecified and systematic approach, avoiding p-hacking. Best-practice methods address measured confounding, and control questions (questions where the answer is known) quantify potential residual bias. Finally, the evidence is generated in a network of databases to assess consistency, by sharing open source analytics code to enhance transparency and reproducibility, but without sharing patient-level information, ensuring patient privacy.

  Discussion: Following guiding principles addressing study bias, p-hacking, and publication bias, LEGEND seeks to generate reliable evidence from existing healthcare data. The principles of LEGEND will be highlighted using an example study on effects of antihypertensives, evaluating internal and external validity of the generated evidence.
  

  Biography

  A/Prof Pratt is an expert in biostatistics and pharmaco-epidemiology, specialising in the development of methodologies to study the effects of medicines and medical devices in linked health-care datasets. Nicole made significant contributions to medication safety surveillance, developing analysis software and pioneering a distributed network model to allow it to be implemented globally. Her research includes knowledge generation and quantification of harms from medicines and devices and has led to TGA safety warnings, changes in practice and changed guidance for medicine use. For the last five years Nicole has worked collaboratively with OHDSI (Observational Health Data Sciences and Informatics) and has helped to develop a comprehensive framework for analysing observational healthcare data at-scale. LEGEND (Large-scale Evidence Generation across a Network of Database) aims to generate real world evidence on the effects of medical interventions using best-practice statistical methodology to support clinical decision making.
  

  Feel free to forward this meeting notice to colleagues, all welcome.

  
  

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• ACEMS' National Science Week Virtual Quiz

  • When
    20 Aug 2020
  •  
    6:00 PM - 7:00 PM (UTC+10:00)
  • Location
    via Zoom, times stated are AEST

  
  

  ACEMS is proud to host a National Science Week Virtual Quiz on Thursday, 20 August from 6 - 7 pm AEST. It will be 60 minutes of fascinating and intriguing science that promises to be fun for everyone to enjoy! While the panel ponders the series of scientific questions posed to them, we're offering you the chance to test your knowledge and win some great prize packs. We hope that it may be of interest to your members and if possible it would be wonderful if you could promote it in your channels and ENewsletter.

  The event is hosted by ACEMS' Anthony Mays and  he will be joined by a great panel including 

  • Dr Gary Beane is a Research Fellow at Monash University and the ARC Centre of Excellence in Future Low-Energy Electronics Technologies (FLEET).
  • Professor Jared Cole is a Theoretical Physicist and Chief Investigator with the ARC Centre of Excellence in Exciton Science at RMIT.
  • Dr Rheanna Mainzer is a Postdoctoral Research Fellow in Biostatistics and Data Science at the Murdoch Children's Research Institute.
  • Dr Jen Martin founded and leads The University of Melbourne's acclaimed Science Communication Teaching Program and is a member of the Homeward Bound Teaching Faculty.
  • Tom Gordon studied Astrophysics, Science Communication, Space Science and Education and is a Senior Science Communicator at the School of Physics at The University of Sydney.

  The Quiz will be delivered by Zoom Webinar, to register click here.

  Kind regards
  
  Rosanna Verde | Outreach Business Officer | ACEMS

  Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers

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• Data and Analytics Leaders Exchange 2020

  • Start
    24 Aug 2020
  •  
    (UTC+10:00)
  • End
    26 Aug 2020
  •  
    (UTC+10:00)
  • Location
    Gold Coast, Australia

  Imagine the benefit you would get from joining a group of 50 peers - who share your biggest challenges - to share stories, network and exchange mission critical information you won’t get anywhere else. Here’s a taste of what you can expect to learn about at the Data & Analytics Leaders Exchange:

  - Developing An Enterprise-Wide Data Governance Strategy And Architecture
  - Enabling Data-Driven Decision Making Across The Business
  - Building Organisational Capability For Advanced Analytics
  - Attracting, Retaining, And Up-Skilling Staff For Digitalisation And Technological Developments
  - Maintaining Compliance With GDPR
  - Improving Business Function By Effectively Leveraging Data Insights

  Request an invite

   

  
  

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• External event: Introductory Statistics for Researchers

  • Start
    24 Aug 2020
  •  
    9:00 AM (UTC+10:00)
  • End
    27 Aug 2020
  •  
    1:00 PM (UTC+10:00)

  Introductory Statistics for Researchers

  Course Overview

  In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?" 

  How does one decide which statistical procedure is the most appropriate? What do all the sections of the output mean? 

  This course is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher. The theory behind the statistical procedures outlined in the course will, in general, not be discussed. 

  Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them. 

  A range of statistical analyses will be discussed in the course, as described in the course outline below. We will talk through examples of all analysis types and will demonstrate how to carry them out in R. Equal emphasis will also be put on interpreting the output of these analyses. There will be plenty of practical work in the course. You will need basic R proficiency to carry out the practical work; we will teach only the additional R commands needed for these analyses. 

  Course Requirements

  You will need to use a computer during the course. You will also need administrator rights to install software needed for the course onto your computer. Previous basic experience using the program R is essential. 

  Date:               Monday 24 to Thursday 27 August, 2020 (four half-day sessions)

  Duration:         9.00 am to 1.00 pm – Each day

  Location:         Online

  For further details and bookings:

  https://www.eventbrite.com.au/e/online-short-course-introductory-statistics-for-researchers-tickets-112412669320

  Stats Central provides study design and analysis support to all UNSW researchers, in collaborative and consultative roles, and conducts short courses and monthly seminars on topical issues. Please see below for our upcoming seminar and short courses. Please feel free to circulate this email to your colleagues, staff, researchers and students, who may be interested. Should you have any questions please don’t hesitate to contact us or visit our website statscentral.unsw.edu.au.

  **Due to the COVID-19 situation, we are offering online course with 20% discounted rates.**

  Note: The courses will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.

  
  

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• Statistical consulting in the 21st century

  • When
    25 Aug 2020
  •  
    11:00 AM - 12:30 PM (UTC+10:00)
  • Location
    Online

  Register

  The past two decades have seen a phenomenal change in the landscape of statistical analysis. Datasets are now available that are of unprecedented volume, complexity in structure, and are coming from designs and sources that present new and exciting challenges.

  What are the challenges in statistical consulting and how are they impacted by this changing landscape? What are the consulting and communication skills needed to support successful consultation? And what has been the impact of the COVID-19 pandemic?

  We bring together four statistical consultants with vast collective experience to share their insights with us. Eminent international statistical consultant, Professor Doug Zahn, invites you to send your stumbling blocks in communication for discussion. Each speaker will present a short talk, followed by a panel discussion.

  This event will be held online and is for members of SSA and NZSA only. Membership with NZSA will be confirmed with NZSA.

  Please register to receive the link to join the event.

  This event is hosted jointly by the Statistical Consulting Network, the Canberra Branch and the Victorian Branch.

  Speakers

  • Elyse Corless, Data Analysis Australia
  • Graham Hepworth, University of Melbourne
  • Hwan-Jin Yoon, Australian National University
  • Doug Zahn, Florida State University

  Moderator: Alice Richardson, Australian National University

  Details of each talk:
  

  An abundance of data

  Data is plentiful in the 21st century and with it comes a range of new opportunities and challenges. Whether data has been manually entered or automated, consideration needs to be given to the accuracy and fit for the questions at hand to make meaningful interpretations. Understanding data quality can be especially challenging with increasingly large and complex datasets and time constraints in a business environment. I will share examples of challenges faced and lessons learned from her wealth of experience.

  
  

  Elyse Corless is a Senior Consultant Statistician at Data Analysis Australia experienced in the areas of applied statistics, surveys, sampling, mapping and forecasting. Examples of the diverse range of projects she has contributed to include community surveys relating to legal applications, forecasting for courts and utility providers and financial assurance activities.

  Statistical consulting: the art of the possible

  In my 3.8 decades of statistical consulting, I have found that good consulting requires attention to quality in several areas simultaneously – these include technical and personal skills, clarity of explanation (verbal and written), organisation, lavish communication, and judgment as to what is needed and what the client can handle. Any of these, if neglected, can mess up a consulting project, but the last aspect is not usually given much attention.

  Having spent equal time consulting in the 20th and 21st centuries (P = 0.87 on a sign test), with the latter obviously affording more new opportunities, I have found that “the art of the possible” is still a useful guiding motivation. While the sky’s the limit (and occasionally is achieved), an early assessment of what is realistically achievable results in a better outcome for the client and less pain for the consultant.

  
  

  Graham Hepworth has worked as a statistical consultant for as long as he can remember – well, almost. After trying his hand as a mathematics teacher, he moved into consulting – at the ABS (2 yrs), in forestry research (5 yrs), agricultural research (11 yrs), and now general consulting (20 yrs and counting – close to a geometric sequence). He has consulted and collaborated in most broad areas of research and investigation, and he particularly enjoys the design and analysis of experiments, and sampling problems.

  Statistical consulting in the university: some challenges

  Statistical consulting in a university context typically involves a collaboration between a statistician and a researcher, with the aim to devise solutions to research problems involving data. The researcher comes with domain expertise in the sciences or social sciences, and the statistician brings their expertise in experimental/survey design and data analysis. A successful collaboration is a complex activity that requires more from the statistician than statistical knowledge. As statistical consultants, we help researchers to understand the statistical framework of their problem so that researchers themselves can take appropriate action and develop greater statistical competence in the process. Our ability to interact empathetically with our clients determine the ultimate success of the consultation.

  This presentation focuses on some (general) challenges associated with statistical consulting at universities, which I will illustrate with stories from my 12 years as a consultant at ANU.

  
  

  Hwan-Jin Yoon joined the Statistical Consulting Unit at The Australian National University in 2008, having previously worked in the Department of Primary Industries in Victoria as a biometrician. He completed his PhD in applied statistics at the University of Melbourne, 2005. He has extensive experience in experimental design, random effects models, statistical moderation, survival analysis, and multivariate analysis in social science, medical science, environmental science, ecology, biology and engineering and computer science.
  Hwan-Jin Yoon is an Accredited Statistician (AStat) with the Statistical Society of Australia.

  What is your most troublesome stumbling block?

  In my experience, we share stumbling blocks. The most effective way of addressing them is in open conversations with colleagues with similar concerns. Please submit a description of your stumbling blocks when you register for this event. We will collate and anonymise them, and explore the most frequent ones in our time together. (Thank you in advance for your commitment of time and energy to our time together.)

  
  

  Doug Zahn earned a doctorate in statistics from Harvard University. He is a professor emeritus of the Florida State University department of statistics, where he taught applied statistics and statistical consulting courses for 35 years. He provided consulting services to faculty and students. For over nine years he coached consulting professionals at the United Kingdom Office for National Statistics while co-teaching a course on consultancy skills. Doug is the co-author of The Human Side of Statistical Consulting and Quality Management Plus: The Continuous Improvement of Education. He and his wife, Andrea, live in Tallahassee, Florida.

  Doug most recently published Stumbling Blocks to Stepping Stones: A Guide to Successful Meetings and Working Relationships (2019). In this book he discusses how to improve your conversations, thereby building more effective relationships, whether with friends or those you perceive as adversaries.

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• External event: Introduction to Regression Modelling in R

  • Start
    31 Aug 2020
  •  
    9:00 AM (UTC+10:00)
  • End
    4 Sep 2020
  •  
    12:30 PM (UTC+10:00)
  • Location
    Online

   Introduction to Regression Modelling in R

  Course Overview

  The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models. Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables you to take a systematic approach to key steps in analysis (like assumption checking) and to extend your skills to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models). 

  This short course, taking place over five half-days, is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools - you should know about the t-test, linear regression, analysis of variance and know something about orthogonal and nested designs. If you have not used R before, we strongly recommend you learn basic features of R and how to use the RStudio interface to R before this course. If you have no experience with R, Stats Central offers an Introduction to R short course, which will be run on 11-12 August and 19-20 August (see above). If you need to revise introductory statistics material, you should attend the Introductory Statistics for Researchers course on 24-27 August (see above), as the material in it is taken as assumed knowledge for this regression course. 

  Course Requirements

  You will need to use a computer during the course. Some familiarity with introductory statistics and R will be assumed. 

  Date:               Monday 31 August to Friday 4 September, 2020 (five morning sessions)

  Duration:         9.00 am to 12.30 pm – Each day

  Location:         Online

  For further details and bookings:

  https://www.eventbrite.com.au/e/online-short-course-introduction-to-regression-modelling-in-r-tickets-112570338914

  Stats Central provides study design and analysis support to all UNSW researchers, in collaborative and consultative roles, and conducts short courses and monthly seminars on topical issues. Please see below for our upcoming seminar and short courses. Please feel free to circulate this email to your colleagues, staff, researchers and students, who may be interested. Should you have any questions please don’t hesitate to contact us or visit our website statscentral.unsw.edu.au.

  **Due to the COVID-19 situation, we are offering online course with 20% discounted rates.**

  Note: The courses will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.

  
  

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• External event: Introduction to Python for Data Science

  • Start
    1 Sep 2020
  •  
    10:00 AM (UTC+10:00)
  • End
    2 Sep 2020
  •  
    1:00 PM (UTC+10:00)
  • Location
    Online

  Introduction to Python for Data Science

  Course Overview

  Python is a widely used programming language to manipulate, analyze, and visualize data. It is one of the most popular languages for Data Science, especially when dealing with complex, uncurated or text datasets. 

  This course assumes that you have never used Python before, but you have some basic programming knowledge. You will learn how to obtain and install Python, which is open-source software, and Jupyter Notebook, which is an interactive computational environment, in which you can combine code execution, rich text, mathematics, plots and rich media. 

  This two half-days introduction to Python will cover some useful features of Python for data science. It will discuss various online resources available to further develop your data science skills using Python.

  Presenter: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)

  Course Requirements: You will need a computer.

  Date:               Tuesday 1 & Wednesday 2 September, 2020 (two half-day sessions)

  Duration:         10.00 am to 1.00 pm – Each day

  Location:         Online

  For further details and bookings:

  https://www.eventbrite.com.au/e/online-short-course-introduction-to-python-for-data-science-tickets-112899814384

  Stats Central provides study design and analysis support to all UNSW researchers, in collaborative and consultative roles, and conducts short courses and monthly seminars on topical issues. Please see below for our upcoming seminar and short courses. Please feel free to circulate this email to your colleagues, staff, researchers and students, who may be interested. Should you have any questions please don’t hesitate to contact us or visit our website statscentral.unsw.edu.au.

  **Due to the COVID-19 situation, we are offering online course with 20% discounted rates.**

  Note: The courses will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.

  
  

  Show details
• SA Branch webinar: Propensity score techniques in multiple treatment framework: the estimation of neighbourhood effect

  • When
    3 Sep 2020
  •  
    6:00 PM - 7:00 PM (UTC+09:30)
  • Location
    virtual Zoom meeting

  Branch Meeting - Thursday, 3rd September 2020

  The South Australian Branch of the Statistical Society would like to invite you to one of two September meetings of the 2020 program.

  Virtual Venue via Zoom meeting. The meeting link will be published shortly.
  

  Speaker: Dr Margherita Silan, Padua University
  

  Topic: Propensity score techniques in multiple treatment framework: the estimation of neighbourhood effect
  

  Abstract

  The study of neighbourhood effects on health conditions has gained attention exponentially during last decades. This topic is even more important from a public health perspective when the focus is on health conditions of elderly, whom spend more time in their neighbourhood than young people. The study estimates neighbourhood effects on elderly health outcomes in Turin (a city located in north Italy) using data coming from the Turin Longitudinal Study, that contains linked individual data from censuses and administrative health data flows. Individuals are not randomly distributed among neighbourhoods, the residential area is chosen also on the basis of individual socio-economic characteristics. This causes a selection bias, that may confound comparisons between the distribution of health outcomes among neighbourhoods. In order to balance observable confounders and to make different populations living in different neighbourhoods comparable, we adopted a propensity score approach. The original methodological contribution of our work is the adaptation of propensity score techniques to a framework with many treatments (that are neighbourhoods in this application). Indeed, Turin may be divided according to three geographical partitions in 10, 23 or 94 neighbourhoods. We proposed a novel method that consists on a Matching on Poset based Average Rank for Multiple Treatments (MARMoT), which has revealed to be really useful to improve the covariates’ balance between groups and pretty fast with respect to other approaches, that have revealed to be unpractical in applications with many treatments.

  Biography

  Margherita Silan is a postdoctoral research fellow at the Department of Statistical Sciences, Padua University. She received a PhD degree from the Padua University, in 2019. Margherita is also the winner of the Italian Statistical Society annual award for best PhD in Applied Statistics. Her research interests include causal inference in multiple treatment frameworks, composite indicators and partially ordered set theory. The application areas are public health, ageing and gender equality.
  

  Feel free to forward this meeting notice to colleagues, all welcome.

  
  

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• External event: Text Analytics in Python (Advanced)

  • Start
    8 Sep 2020
  •  
    10:00 AM (UTC+10:00)
  • End
    9 Sep 2020
  •  
    1:00 PM (UTC+10:00)
  • Location
    Online

  Text Analytics in Python (Advanced)

  Course Overview

  More than 70% of the data on the internet is unstructured. Among them, text is the most common form that appears in almost all data sources. For example, text data such as emails, online reviews, tweets, news and reports hold valuable information and insight for most research and applications. Text analytics, usually involving techniques from text mining or natural language processing (NLP), can automatically uncover patterns and extract meaning/context from these unstructured texts. 

  This course assumes that you have basic Python programming knowledge, or have previously attended "Introduction to Python for Data Science" from Stats Central. This course will provide you the foundation to process and analyze text. 

  In this course, we will cover some useful Python features and libraries for text processing and analysis. We will touch on some advanced topics such as sentiment analysis, text classification, and/or topic extraction. 

  Presenter: A/Professor Raymond Wong (Stats Central and UNSW School of Computer Science and Engineering)

  Course Requirements: You will need a computer.

  Date:               Tuesday 8 & Wednesday 9 September, 2020 (two half-day sessions)

  Duration:         10.00 am to 1.00 pm – Each day

  Location:         Online

  For further details and bookings:

  https://www.eventbrite.com.au/e/online-short-course-text-analytics-in-python-advanced-tickets-112901224602

  Stats Central provides study design and analysis support to all UNSW researchers, in collaborative and consultative roles, and conducts short courses and monthly seminars on topical issues. Please see below for our upcoming seminar and short courses. Please feel free to circulate this email to your colleagues, staff, researchers and students, who may be interested. Should you have any questions please don’t hesitate to contact us or visit our website statscentral.unsw.edu.au.

  **Due to the COVID-19 situation, we are offering online course with 20% discounted rates.**

  Note: The courses will be delivered remotely using online collaborative teaching tools, with a mix of live video-assisted lectures and computer-based tutorials. A continuous chat session will be available and there will be extra staff to answer questions during the course.

  
  

  Show details
• Webinar: Random Effects Inference in Linear Mixed Models: The good, the bad, and the misspecified

  • When
    25 Sep 2020
  •  
    12:00 PM - 1:00 PM (UTC+10:00)
  • Location
    via Zoom
  • Spaces left
    286

  Register

  You are warmly invited to the following webinar hosted by SSA:

  Random Effects Inference in Linear Mixed Models: The good, the bad, and the misspecified

  held on Friday, 25 September 2020 at 12:00PM AEST via Zoom, exclusively for members of SSA.

  This event is presented by Francis K.C. Hui and Alan H. Welsh (Research School of Finance, Actuarial Studies & Statistics, Australian National University).

  About this webinar:

  In many applications of mixed models, the scientific questions of interest can often relate to estimation and inference of the random effects . Examples range from assessing the importance of all or a subset of the variance components, constructing functions and associated uncertainty intervals of functions of the random effects and/or variance components, to directly modelling the random effects as a function of other covariates. On the other hand, unlike fixed effects inference there is a lot more contention and uncertainty (pun intended!) surrounding both how to do random effects inference in mixed models, and what the consequences are of associated misspecification on estimation and inference.

  This talk is a culmination of two projects on the topic of random effects inference in linear mixed models. In the first half, we re-examine one of the earliest and simplest methods of random effects testing in linear mixed models, namely the F-test based on linear combinations of the responses, or FLC test. For current statistical practice, we argue that the FLC test is underused and should be given more consideration especially as an initial or “gateway” test for linear mixed models. We discuss three advantages of the FLC test often overlooked in modern applications of linear mixed models: computation speed, generality, and its exactness as a test. In the second half, we examine some impacts of random effects misspecification on random effects inference in linear mixed models. While some large sample results can be formulated, we show that in general that incorrectly assuming a normal distribution for the random effects can have severe consequences for random effects inference, with strongly biased estimation of variance components and associated likelihood-based confidence intervals for the variance components exhibited potentially severe under coverage.

  If time permits, we conclude our talk by encouraging more general discussion and research on the notion of sometimes treating random effects as fixed effects for estimation and inference, and how the above findings may generalise far beyond linear mixed models.

  About the presenters:

  Francis K.C. Hui and Alan H. Welsh (Research School of Finance, Actuarial Studies & Statistics, Australian National University)

  Francis Hui is a Senior Lecturer in Statistics at the Australian National University. He completed his PhD at the University of New South Wales in 2014 and moved to Canberra to undertake a postdoctoral fellowship at the ANU, and has been willingly stuck there since. His research spans a mixture of methodological, computational, and applied statistics, including longitudinal and correlated data analysis, dimension reduction and variable selection, and approximate statistical estimation and inference. Much of his research is motivated by joint modelling in ecology, and longitudinal analysis of mental health data, and is complemented by copious amounts of tea drinking, and unhealthy amounts of anime watching.

  Alan Welsh, recipient of the Pitman Medal in 2012, is the E.J. Hannan Professor of Statistics at the Australian National University. He has been at ANU for a while, spending time in the Mathematical Sciences Institute and the Research School of Finance, Actuarial Studies & Statistics. He has also held positions at the University of Chicago and the University of Southampton. His research interests include statistical inference, statistical modelling, robustness, nonparametric and semiparametric methods, analysis of sample surveys, and ecological monitoring. Much of his recent work has gone into trying to understand linear mixed models better.
  

  Registration

  This event is for members of SSA and members of NZSA. (If you register as a member of NZSA please note that we will confirm your membership with our colleagues of the NZSA). The event is free, but you will need to register. Registration is a 2-step process. Please use the registration link on the left to register with SSA. You will receive a confirmation email containing a link for the registration with Zoom.  Please complete the registration with Zoom at your earliest convenience as places on our Zoom platform are limited!

  This event will be recorded and the recording added to SSA’s webinar page in due course after the event.

  Would you please note that the times stated are AEST?

  Cancellation Policy

  Our Guarantee
  Our goal is to provide truly exceptional offerings and service, and we won't be happy until you are. If any programs, products or services of SSA do not fulfill our promise, we will make the situation right.

  Event Cancellation or Postponement
  SSA reserves exclusive right to modify, postpone/reschedule or cancel programs for any reason, including but not limited to emergency, inclement weather or other 'acts of God'. If there is an event cancellation, every attempt will be made to reschedule, and registration fees will be applied to the rescheduled event date. Any travel, lodging, or incidental expenses incurred related to a cancelled event cannot be refunded under any circumstances. 

  Consent to Use of Photographic Images
  Registration and attendance at, or participation in, SSA meetings and other activities constitutes an agreement by the registrant to the use and distribution of the registrant or attendees' image or voice in photographs, videotapes, electronic reproductions and audiotapes of such events and activities by SSA. 

  If you have any questions, please contact Marie-Louise Rankin, SSA Executive Officer.

  
  

  Show details
• Mathematics without Borders

  • Start
    28 Sep 2020
  •  
    (UTC+02:00)
  • End
    29 Sep 2020
  •  
    (UTC+02:00)
  • Location
    Strasbourg, France

   The Centennial of the International Mathematical Union

  As you may know, the IMU was founded in September 1920 in Strasbourg, France. On  the occasion of the centennial of the IMU, we are organizing the conference               

   Mathematics without Borders
  The Centennial of the International Mathematical Union             

  You are cordially invited to participate in this conference. We will get back

  with further information regarding registration, accommodation, etc as

  soon as it is available. 

   

  
  

  Show details
• WA Young Statisticians Workshop 2020

  • Start
    30 Sep 2020
  •  
    (UTC+08:00)
  • End
    1 Oct 2020
  •  
    (UTC+08:00)
  • Schedule
    2 sessions
  • Location
    Perth

  UPDATE: We are currently exploring the format and pricing of this meeting due to the COVID-19 situation. Registration is therefore temporarily disabled.

  Don’t miss out on your chance to meet other young statisticians and leading members of WA’s statistical and data science community. Speakers from industry and academia.

  Young Statisticians are invited to submit an abstract to present a short talk or poster on your research with statistics to win prizes! Please send your queries to ysmurdoch@gmail.com

  Show details
• CPD120: Online workshop - Semiparametric regression with R

  • Start
    30 Sep 2020
  •  
    11:00 AM (UTC+10:00)
  • End
    2 Oct 2020
  •  
    1:00 PM (UTC+10:00)
  • Location
    via Zoom
  • Spaces left
    22

  Register

  Please join us for the following online workshop

  Semiparametric Regression with R

  to be held over three days:

  11:00am-1:00pm (Australian Eastern Standard Time) Wednesday 30th September 2020

  11:00am-1:00pm (Australian Eastern Standard Time) Thursday 1st October 2020

  11:00am-1:00pm (Australian Eastern Standard Time) Friday 2nd October 2020
  

  About the presenter

  Matt P. Wand is a Distinguished Professor of Statistics at the University of Technology Sydney. He has held faculty appointments at Harvard University, Rice University, Texas A&M University, the University of New South Wales and the University of Wollongong. Professor Wand is an elected fellow of the Australian Academy of Science, the American Statistical Association and the Institute of Mathematical Statistics. He was awarded two of the Australian Academy of Science's medals for statistical research: the Moran Medal in 1997 and the Hannan Medal in 2013. In 2014 he was awarded the Statistical Society of Australia's Pitman Medal. He has served as an associate editor for several journals including Biometrika, Journal of the American Statistical Association and Statistica Sinica. He has co-authored 2 books, more than 120 journal articles and 6 R packages on semiparametric regression and related areas.

  Further details about Professor Wand’s research, and every paper he has written, are on the website.

  
  

  About the course

  Semiparametric regression methods build on parametric regression models by allowing more flexible relationships between the predictors and the response variables. Examples of semiparametric regression include generalized additive models, additive mixed models and spatial smoothing. The presenter's goal is to provide an easy-to-follow applied course on semiparametric regression methods using R. There is a vast literature on the semiparametric regression methods. However, most of it is geared towards researchers with advanced knowledge of statistical methods. This course is intended for applied statistical analysts who have some familiarity with R.

  This short course explains the techniques and benefits of semiparametric regression in a concise and modular fashion. Spline functions, linear mixed models and Bayesian hierarchical models are shown to play an important role in semiparametric regression. There will be a strong emphasis on implementation in R and rstan with most of the short-course spent doing computing exercises.

  The workshop is based on the in-press book “Semiparametric Regression with R” by  J. Harezlak, D. Ruppert and M.P. Wand (Springer, 2018), with website http://semiparametric-regression-with-r.net/, and has the companion methodology and theory book “Semiparametric Regression” by D. Ruppert, M.P. Wand and R.J. Carroll (Cambridge University Press, 2003), with website.

  Target Audience

  Most of the course will be geared towards researchers with intermediate to advanced knowledge of statistical, particularly regression, methods.

  Learning Objectives

  1. Introduction to semiparametric regression at the applied level

  2. Implementation of the presented methods in R and rstan

  3. Application of the newly learned methods to a variety of datasets

  
  Fees

  Early Bird rates (Registration and payment before 1 September)
  

  Early Bird SSA Members	$150
  Early Bird Non-Members	$390
  Early Bird Students	$100
  Early Bird Non-Member Students	$130

  
  

  Rates from 1 September 

  SSA Members	$200
  Non-Members	$440
  SSA Student Members	$150
  Non Member Students	$180

  
  

  Cancellation Policy

  Cancellations received prior to Wednesday, 23 September 2020 will be refunded, minus a $25 administration fee.

  From then onwards no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to eo@statsoc.org.au.

  Our Guarantee
  
  Our goal is to provide truly exceptional offerings and service, and we won't be happy until you are. If any programs, products or services of SSA do not fulfill our promise, we will make the situation right.

  Event Cancellation or Postponement

  SSA reserves exclusive right to modify, postpone/reschedule or cancel programs for any reason, including but not limited to emergency, inclement weather or other 'acts of God'. If there is an event cancellation, every attempt will be made to reschedule, and registration fees will be applied to the rescheduled event date. Any travel, lodging, or incidental expenses incurred related to a cancelled event cannot be refunded under any circumstances. 

  Consent to Use of Photographic Images
  
  Registration and attendance at, or participation in, SSA meetings and other activities constitutes an agreement by the registrant to the use and distribution of the registrant or attendees' image or voice in photographs, videotapes, electronic reproductions and audiotapes of such events and activities by SSA. 

  Show details
• CPD121 - Workshop: Data visualisation with R

  • Start
    11 Nov 2020
  •  
    (UTC+11:00)
  • End
    12 Nov 2020
  •  
    (UTC+11:00)
  • Schedule
    2 sessions
  • Location
    Online
  • Spaces left
    19
  • Registered
    1 registrant

  Register

  Statistical Society of Australia warmly invites you to a workshop on data visualisation with R, taught by Prof. Di Cook and Dr. Emi Tanaka.

  This is a repeat of the July workshop. Please note there may be slight changes based on past participant feedback.

  About the workshop:

  Data visualisation is a key statistical tool for effective communication and to understand aspects of data and models. The statistical language R is used widely for data analysis and visualization, e.g. the BBC Visual and Data Journalism team uses ggplot2 R-package to create production-ready charts. This workshop (on Day 1) will teach you how to create production-ready graphics using the grammar of graphics implemented in ggplot2 R-package. In addition, the workshop (on Day 2) will teach you how to construct more complex plots, including maps, and discuss inference for statistical graphics to understand if what we see in a plot is really there. The workshop will be hands-on with plenty of practical examples.

  About the presenters:

  Dianne Cook is Professor of Business Analytics at Monash University in Melbourne, Australia.  She is a world leader in data visualisation, especially the visualisation of high-dimensional data using tours with low-dimensional projections, and projection pursuit.  She is currently focussing on bridging the gap between exploratory graphics and statistical inference.  Di is a Fellow of the American Statistical Association, past editor of the Journal of Computational and Graphical Statistics, current editor of the R Journal,  elected Ordinary Member of the R Foundation, and elected member of the International Statistical Institute. 

  Emi Tanaka is a Lecturer in Statistics at Monash University and the Vice President of SSA Vic. She is currently working on a statistical theory for conducting inference using data plots and is an early career researcher in multi-level modelling, and experimental design. She is an experienced and enthusiastic R user and instructor, and regularly teaches university courses and workshops to the broader community on data visualisation, including ggplot2.

  Target audience:

  Day 1 is suitable for those who know R but are not familiar or comfortable with using ggplot2 or would like a refresher on ggplot2. 

  Day 2 is aimed for those that have familiarity with ggplot2 but would like to delve deeper into advanced plotting techniques, including interactive plots and animating plots, and using plots for inference. 

  Learning objectives:

  Day 1: Dipping Your Toes into Data Visualization with R

  Presented by Emi Tanaka
  

  • Review of tidy data format

  • Basics of the grammar of graphics

  • Drawing the basic data plot types (barchart, pie chart, histogram, density plot, scatterplot, boxplot) utilising a range of common geoms and variable mappings

  • Choosing colour wisely

  • All about scales, transforming data, setting limits, changing coordinate systems, axis specifications, ordering levels of categorical axes

  • Jazzing up your plot with different themes, plot annotations and combining plots together to make a publication-ready plot

  Day 2: Diving Deeper into Data Visualization with R

  Presented by Di Cook
  

  • Check your knowledge, a review of basic plotting with the grammar

  • Expanding your graphics toolbox to mapping, making choropleth maps, using map images as a base

  • Making your plots speak. Adding interactive elements including mouse-over labels, and sliders for controlling parameters, using plotly. Animating plots using gganimate.

  • Learn how to decide on the best plot design for a problem, and how to  determine if what you see is a real structure.  

  Requirements:

  Day 1

  • basic R knowledge (e.g. you have used R to load data, create simple visualisations, perform basic analyses and write simple functions or more specifically, you are familiar with concepts in Cookbook for R by Winston Chang)

  • basic statistics (e.g. simple linear regression, hypothesis testing, basic summary statistics and plots)

  • computer (with ability to install R and R-packages), microphone and web camera 

  • stable internet connection

  • Install the video conferencing software, zoom and know how to use zoom

  Day 2:

  • Day 1 requirements

  • Completed Day 1 OR know basic ggplot2

  Desirable:

  • Know about tidy data (i.e. importing data and putting data into the right format for plotting)

  • Some familiarity with tidyverse

  Timetable
  

  Please note that we may modify the schedule slightly after feedback from our July workshop.

  Day 1

  1:30pm – 3.00pm (1.5 hours)	Session 1
  3.00pm – 3.30pm	Break / networking over virtual morning tea
  3.30pm – 5:00pm (1.5 hours)	Session 2
  12:30pm	End of first day

  Day 2

  1.30pm – 3.00pm (1.5 hours)	Session 1
  3.00pm – 3:30pm	Break / networking over virtual morning tea
  3:30pm – 5:00pm (1.5 hours)	Session 2
  5:00pm	End workshop

  
  Deadlines: Early Bird registration closes on 22 Sep 2020. Regular registrations close on midnight on 3 Nov 2020.

  Expenses:

  Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen. Please note that the Society will not be held responsible for any financial loss incurred due to a workshop cancellation.

  Financial Support:

  Financial support for SSA Vic members can be sought. For further information, please see: https://sites.google.com/view/ssavicworkshopfinsup.

  Cancellation policy:

  Cancellations received prior to 20 Oct 2020 will be refunded, minus a $20 administration fee. From then onwards no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to eo@statsoc.org.au..

  Contact:

  Please contact the organisers: Rheanna Mainzer (rheanna.mainzer@gmail.com), Lidija Turkovic (lidijat@gmail.com), and Emi Tanaka (dr.emi.tanaka@gmail.com) for further details.

  Show details
• SSA Canberra Knibbs Lecture: Prof Ray Carroll

  • When
    24 Nov 2020
  •  
    12:30 PM - 2:00 PM (UTC+11:00)
  • Location
    Online

  SSA Canberra invites you to attend this year's Knibbs lecture, which will be presented by Prof. Ray Carroll (Texax A&M). The lecture will also celebrate the 60th birthday of Prof. Alan Welsh (ANU), and his contributions to statistics. 

  
  

  Date:  Tuesday 24th November

  Time: 12:30pm-1:45pm AEST.  

  More details will be announced closer to the date of the event, including registration to attend the Zoom meeting.

  
  

  Show details
• 7th Biennial ACSPRI Social Science Methodology Conference 2020

  • Start
    2 Dec 2020
  •  
    (UTC+11:00)
  • End
    4 Dec 2020
  •  
    (UTC+11:00)
  • Location
    Sydney, Australia

  The Australian Consortium of Social and Political Research Incorporated (ACSPRI) will host it's 7th Social Science Methodology Conference on the 2nd to 4th of December at The University of Sydney.

  A unique feature of this conference is that it is multi-disciplinary and brings together researchers and methodologists from a range of environments and contexts.

  The conference is organised around four themes:

  1. Research paradigms and designs;
  2. Research methods and techniques;
  3. Research technology and tools;
  4. Datasets, data collections and data archiving.

  There will be two types of submissions considered:

  1. Presentations where an abstract is reviewed (abstract in the conference proceedings).
  2. Posters - including a student poster competition (abstract in the conference proceedings).

  More details will appear on the conference website soon. 

  Show details
• SSA-Event: ANZSC2021- Modelling Data for a Brighter Future

  • Start
    5 Jul 2021
  •  
    8:00 AM (UTC+10:00)
  • End
    9 Jul 2021
  •  
    6:00 PM (UTC+10:00)
  • Location
    Gold Coast Convention Centre, 2684 -2690 Gold Coast Highway, Broadbeach, QLD

  
  

  Welcome to the 
  ANZSC 2021 Conference
  

  The organising committee warmly invites you to the 2021 Australian and New Zealand Statistical Conference, which will take place on the Gold Coast from the 5th to the 9th of July 2021.

  This conference brings together four leading statistical communities in the region – the Statistical Society of Australia, the New Zealand Statistical Association, the International Institute of Business Analysis (Special Interest Group for Business Analytics), and the Australian Conference on Teaching Statistics.

  The aim of this conference is to bring together a broad range of researchers and practitioners across a variety of statistical disciplines to facilitate the exchange of theory, methods and applications.

  With these four societies working together there will be strong program components of interest to a wide diversity of academic, government, and industry colleagues. This includes the full spectrum of delegates from those advancing theoretical methodology to those working on industry applications (in traditional and non-traditional statistical areas). Of particular interest is how Big Data continues to impact all of us.

  Information on Keynote Speakers and the Conference program will be available shortly so watch this space for updates.

  The conference will be held at the Gold Coast Convention and Exhibition Centre (GCCEC) situated in the heart of the Gold Coast. From GCCEC, Surfers Paradise (the social hub of the Gold Coast) is 5km to the North, the Star Casino and Pacific Fair are immediately to the South (the largest regional shopping and dining destination in Queensland), the beach (Broadbeach) is just ten minutes walk, and the Broadbeach restaurant complex is immediately to the East (short 5 minutes walk). Social tours can easily be made to the rainforest (such as Tambourine National Park and World Heritage-listed Lamington National Park), to places of Aboriginal Indigenous significance, to Stradbroke Island, and to Australia’s greatest theme parks.

  ANZSC2021 promises to be a truly amazing experience on both a professional and a social level.

  Please check out the official conference website and register your interest here.

  We look forward to seeing you on the Gold Coast in 2021!

  Show details
• 63rd ISI World Statistics Congress 2021

  • Start
    11 Jul 2021
  •  
    (UTC+10:00)
  • End
    15 Jul 2021
  •  
    (UTC+10:00)
  • Location
    The Hague, The Netherlands

  The 63rd ISI World Statistics Congress will bring together statisticians and data scientists from academia, official statistics, health sector and business, junior and senior professionals, in an inviting environment.

  The inspiring and interactive programme will provide the platform to learn about the latest developments in statistical research and practice in an informal ambiance. A series of short courses, satellites and other events completes the WSC programme.

  Show details
• Mathematics without Borders - The Centennial of the International Mathematical Union

  • Start
    27 Sep 2021
  •  
    (UTC+10:00)
  • End
    28 Sep 2021
  •  
    (UTC+10:00)
  • Location
    Strasbourg, France

  Mathematics without Borders
  
  The Centennial of the International Mathematical Union         

  celebrates the centennial of this historic event.  Up-to-date information regarding the conference can be found here. 

  
  
  

  
  

  Show details
• Joint Southern Statistical Meetings 2022 (JSSM2022)

  • Start
    27 Jun 2022
  •  
    (UTC+10:00)
  • End
    1 Jul 2022
  •  
    (UTC+10:00)
  • Location
    Darwin, Australia

  The inaugural

  Joint Southern Statistical Meetings 2022

  will be held in Darwin from 27 June – 1 July 2022.

  

  This conference will bring together the leading statistical communities in the region to provide a forum for researchers and practitioners across a variety of statistical disciplines to facilitate the exchange of theory, methods and applications.

  To be kept up to date with our conference planning, please email your details to JSSM2022@gmail.com.

  We invite regional associations to contact us with expressions of interest to be part of this event. If you would like to sponsor JSSM2022 please get in touch as well.

  See you in Darwin in 2022! 
  

  Show details
• ICM 2022 - external conference

  • Start
    6 Jul 2022
  •  
    (UTC+10:00)
  • End
    14 Jul 2022
  •  
    (UTC+10:00)
  • Location
    St Petersburg, Russia

  The ICM 2022 (International Congress of Mathematicians) will take place 6–14 July 2022 in St. Petersburg, Russia.
  
  The 19th General Assembly of the IMU will be held in St. Petersburg, on 3–4 July 2022. 
  The official website of the Congress is https://icm2022.org
  

  Show details
• XXXI International Biometric Conference (IBC 2022) - external event

  • Start
    10 Jul 2022
  •  
    (UTC+10:00)
  • End
    15 Jul 2022
  •  
    (UTC+10:00)
  • Location
    Riga, Latvia

  to be held at the Radisson Blu Latvija Conference & Spa Hotel

  
  

  Show details