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    • 5 Oct 2022
    • 4:00 PM - 6:00 PM
    • Online
    Register

    Please join us online for an October Queensland Branch Meeting, on the 5th of October. The seminar will start at 4:00pm, with a branch meeting starting at 5:00pm. Please find details for the seminar below.

    TITLE: clusterBMA: Combine insights from multiple clustering algorithms with Bayesian model averaging

    SPEAKER: Owen Forbes

    TIME: 4:00 PM - 6:00 PM (AEST), Wednesday 5th October 2022

    VENUE: Online (Zoom details will be sent with registration)

    Please note that the seminar will be recorded and might be put on YouTube or similar platform.


    ABSTRACT:

    In this talk, I will introduce clusterBMA, a novel Bayesian Model Averaging (BMA) methodology that combines inference across multiple algorithms for clustering of a given dataset. BMA offers some attractive benefits over other existing approaches for ensemble or consensus clustering. Benefits include intuitive probabilistic interpretation of an overall cluster structure integrated across multiple sets of clustering results, flexibility to accommodate various input algorithms, and quantification of model-based uncertainty. We present results from a substantive neuroscience case study, and two simulation studies. This method is implemented in the freely available R package “clusterBMA”, which will be demonstrated during the talk and can be accessed at https://github.com/of2/clusterBMA


    SPEAKER'S BIO:

    Owen is a final year PhD student under the supervision of Distinguished Professor Kerrie Mengersen at QUT. His research is in applied statistics and neuroscience, studying electrical activity in young people's brains to better understand mental health outcomes and brain development through adolescence. This work contributes to methodology and applications for understanding brain characteristics and mental health through adolescent development, with clinical relevance for risk prediction and early intervention. He is also working on several collaborative applied data science projects in the areas of child health and Indigenous education. As an advocate for mental health action, Owen is also a leader in student-driven wellbeing initiatives, a Peer Support Officer in the ACT State Emergency Service, and a volunteer with Palliative Care ACT. He is passionate about achieving positive societal impacts through data science and research implementation.


    • 11 Oct 2022
    • (AEDT)
    • Online

    The Caucus for Women in Statistics (CWS) and the Portuguese Statistical Association (SPE) are proud to announce the launching of the International Day for Women in Statistics and Data Science (IDWSDS) with its first annual celebration event on October 11, 2022 (the second Tuesday of October). They hope that this will be the annual event for the celebration of women statisticians and data scientists around the world.

     

    WHY IDWSDS?


    Women are under-represented in Statistics and Data Science. There have been global efforts to increase women in STEM disciplines such as the UNs International Day of Women and Girls in Science, the International Day for Women in Mathematics, and ISIs International Year of Women in Statistics and Data Science. The responsibility to increase diversity, inclusion and equity falls on each one of us, as we strive to ensure greater future representation among the younger generations within our profession. We must act now. Like the UN and mathematical societies, They propose to create an International Day of Women in Statistics and Data Science (IDWSDS). They will have a virtual conference to celebrate women statisticians and data scientists on the second Tuesday of October.

     

    WHAT IS THE IDWSDS?


    The IDWSDS will promote and celebrate women in statistics and data science all around the globe with a conference. The aims are to:

    ·          Showcase women and their contributions to the field

    ·          Connect women statisticians and data scientists around the world

    ·          Encourage collaborations among statistical societies around the world

    ·        Impact statistics and data science to become more inclusive and diverse

    ·          Bridgestatistics and data science

     

     For more information click here.


    Note that this will be a virtual event in the spirit of the first Around the World conference which means there will be a place for both live and recorded presentations and sessions and other contributions.

     

    Because time flies, do start planning this event in advance. Please check our Twitter account @cwstat for updates and the website closer to the event.  Submit your session ideas and the society s session sponsorship to idwsds1@gmail.com.  Thank you in advance for your support!



    • 11 Oct 2022
    • 6:00 PM - 7:00 PM (AWST)
    • Cheryl Praeger Lecture Room, The University of Western Australia
    Register

    Announcing the October meeting of the WA Branch of the Statistical Society of Australia. All visitors are welcome to attend this event.

    Date: Tuesday, 11 October 2022.
    Time: 6:00PM.
    Location: Cheryl Praeger Lecture Room, The University of Western Australia.

    We are pleased to host our guest speaker Dr Farzana Jahan of Murdoch University.


    Presentation

    Evaluation of spatial Bayesian Empirical Likelihood models in analysis of small area data

    Dr Farzana Jahan, Murdoch University.

    Bayesian empirical likelihood (BEL) models are becoming increasingly popular as an attractive alternative to fully parametric models. However, they have only recently been applied to spatial data analysis for small area estimation. This study considers the development of spatial BEL models using two popular conditional autoregressive (CAR) priors, namely BYM and Leroux priors. The performance of the proposed models is compared with their parametric counterparts and with existing spatial BEL models using independent Gaussian priors and generalised Moran basis priors. The models are applied to two benchmark spatial datasets, simulation study and COVID-19 data. The results indicate promising opportunities for these models to capture new insights into spatial data. Specifically, the spatial BEL models outperform the parametric spatial models when the underlying distributional assumptions of data appear to be violated.

    About the Speaker

    I am a passionate about statistics and data science teaching and research. I have over 3 years experience in university teaching in Australia and experience of over 3 years in Bangladesh. I recently completed my PhD from Queensland University of Technology, Queensland, Australia. My PhD research was about Bayesian spatial modelling for small area level data. My research interests include: Bayesian Statistics, spatial modelling, modelling aggregated level data. The area of application includes but not limited to health and environment. I wish to balance between teaching and research and contribute to Statistics and Data Science discipline by getting involved in collaborative projects.

    Refreshments and Dinner

    Members and visitors are invited to mingle over wine and cheese from 5:30PM onwards at the venue.

    Following the meeting all are invited to dine at a nearby restaurant. Visitors are welcome.

    Meeting directions

    The Cheryl Praeger Lecture Room is located on the ground floor of the Mathematics building at The University of Western Australia. Its entrance is on the northern side of the building. See: UWA Maps, Google Maps.

    Parking is free on the UWA Crawley campus after 5:00PM. A convenient place to park is Car Park 18 accessible from Fairway Entry 1.

    Remote Viewing Option

    For those that cannot attend in-person, the presentation will be streamed live over Zoom. Please register on this page to get the connection details.

    For further information please contact the WA Branch Secretary (ssa.wa.secretary@gmail.com).

    • 12 Oct 2022
    • 10:00 AM - 4:00 PM (AEDT)
    • Online
    • 27
    Register

    The NSW branch is offering DIY R Package workshop.

    Do you have a few custom functions on heavy rotation? Perhaps you have a piece of code that you regularly share with colleagues? Maybe you’ve developed a new statistical model and want to share it with the world? Why not put it all in an R package?! This interactive workshop will equip you with the basic skills to create an R package of your own! We will walk through the package building process and apply the same workflow to your own function. We will learn about testing and continuous integration and implement them using Github Actions.

    Participants will need to bring along one function they would like to put in a package.

     Prior to the workshop participants will need to have

    1. a Github account;
    2. the most recent version of R and RStudio installed;
    3. the following package(s) and their dependencies installed: devtools, testthat, knitr. 

    Any questions about the workshop, feel free to reach out to Dr Fonti Kar!

    About the Presenter:

    Fonti is a postdoctoral research fellow at University of New South Wales, Sydney, Australia. She is an evolutionary biologist wearing R developer shoes. Fonti works alongside researchers to develop R software tools for the research community. Her interests include biostatistics, reproducible science, learning about the latest coding practices and teaching others to enjoy using R.

     Course Outline

    • Why make an R package? A brief group discussion
    • The toolkit
    • Practical: Code along and make “ohwhaley”
      • R package skeleton
      • Add a function
      • Document it
      • READMEs Long form documentation
      • A basic test
    • Practical: BYO function and make your own package
    • Continuous integration and its benefits for an R package
    • Practical: Setting up Github Actions for your own package
    • Where to get help?
    • BONUS (Time and pace permitting: Setting up a landing website for your own package)

    Course Timetable

    Below is the tentative schedule for the workshop,. It will be adapted on the workshop day. 

    10:00 – 11:00 (1 hour)

    Session 1

    11:00 – 11:30 (30 mins)

    Break

    11:30 – 12:30 (1 hour)

    Session 2

    12:30– 13:30 (1 hour)

    Long Break

    13:30 – 14:30 (1 hour)

    Session 3

    14:30 – 15:00 (30 mins)

    Break

    15:00 – 16:00 (1 hour)

    Session 4


    Learning Objectives

    • Create a skeleton for an R package in making in RStudio IDECreate and document a function
    • Understand the differences between a landing page, short- and long- form documentation
    • Document functions using roxygen2 package
    • Create a vignetteWrite tests for a function using testthat package
    • Integrating functions from devtools and usethis packages to create a simple package
    • Understand the benefits of continuous integration
    • Implement continuous integration with Github Actions

     Link where prerequisite software can be downloaded from:

     R:
    https://cran.csiro.au/

     RStudio:

    https://www.rstudio.com/products/rstudio/download/#download

    To install the above R packages: Go to R console and type:
    install.packages(c(“devtools, testhat”))

    Github Account:
    https://github.com/ and click Sign Up on the top right corner for a Personal account

    • 12 Oct 2022
    • 11:00 AM (AEDT)
    • 7 Dec 2022
    • (AEDT)
    • weekly online
    • 0
    Join waitlist

    “The demand for this course has exceeded available spaces in October. However, if there is sufficient interest we will be looking to repeat the course early in 2023. To express interest in participating in 2023 please click on Join waitlist to add your contact details so that we can notify you when the details for the next offering of this course are available or should spaces free up in October due to cancellations.”


    The Social Research Centre and the Statistical Society of Australia (SSA) are very proud to offer statistical training from the International Program in Survey and Data Science (IPSDS), a joint program of the University of Mannheim and the Joint Program in Survey Methodology at the University of Maryland.

    About the course

    The 8-week online sampling course is aimed at working professionals who are interested in expanding their data collection skills. The course will be available fully online with prerecorded videos that students are expected to watch on their own time plus weekly 1-hour live interactive sessions with the instructor, Raphael Nishimura, Director of Sampling Operations at the University of Michigan.


    About the presenter

    Raphael Nishimura is the Director of Sampling Operations at the Survey Research Operations in the Survey Research Center at the Institute for Social Research from the University of Michigan. He has PhD in Survey Methodology from the Michigan Program in Survey Methodology and a Bachelors degree in Statistics from the Institute of Mathematics and Statistics at the University of Sao Paulo. 

    His research interests are survey sampling, responsive designs and nonresponse adjustments (weighting and multiple imputation).

    In particular, Raphael is interested in methods that mitigate survey nonresponse during data collection using different approaches, such as responsive design and multiple imputation models for nonignorable missing data.

    His dissertation topic was about the use of substitution of nonresponding units in probability-based samples.

    Course dates

    Course dates: Oct 12 – Dec 7, 2022
    Weekly live sessions with instructor: Thursdays, 11am AEDT

    Early Bird Deadline
    Please book before 30 June 2022 to take advantage of the Early Bird Deadline.

    Course objectives

    By the end of the course, you will…

    • understand the basic ideas, concepts and principles of probability sampling from an applied perspective
    • be able to identify and appropriately apply sampling techniques to survey design problems
    • be able to compute the sample size for a variety of sample designs
    • understand and be able to assess the impact of the sample design on survey estimates
    • be able to estimate the precision of the survey statistics using different estimation techniques

    Grading

    Grading will be based on:

    • Homework assignments (50% of the grade)
    • Quizzes (15% of the grade)
    • Participation in discussion during the weekly online meetings, submission of questions demonstrating understanding of the required readings and video lectures and positive contributions on Piazza (10% of grade)
    • A final open-book online exam (25% of grade)

    The course is graded to meet requirements of IPSDS and can be credited towards a certificate or master's degree at JPSM.

    Prerequisites

    The course is presented at an intermediate statistical level. While we will not develop mathematical aspects of sampling theory, statistical notation and outlines of some algebraic proofs will be given. A sound background in applied statistics, proficiency in mathematics, including basic algebra, is necessary, since some algebraic derivations will be presented (although little emphasis will be placed on the derivations). A thorough understanding of the notation and algebraic results will be required.

    Readings:

    Survey Sampling by Leslie Kish (John Wiley and Sons, Inc., New York, 1965).

    Weekly online meetings & assignments:

    • Week 1: Introduction; Course Perspectives (Quiz 1, Assignment 1)
    • Week 2: Simple Random Sampling, Sampling Frames, and Introduction to Clustering (Quiz 2, Assignment 2)
    • Week 3: Stratified Sampling I (Quiz 3, Assignment 3)
    • Week 4: Stratified Sampling II, Systematic selection (Quiz 4, Assignment 4) 
    • Week 5: Cluster Sampling (Quiz 5, Assignment 5) 
    • Week 6: Unequal-Sized Clusters I (Quiz 6, Assignment 6) 
    • Week 7: Unequal-Sized Clusters II (Quiz 7, Assignment 7) 
    • Week 8: Variance Estimation (Quiz 8, Assignment 8)
    • Final exam
    Disclaimer:
    • Participants will receive access data for the online course, in particular to any learning platform that may be used. The rights of use connected to the access data are personally assigned to the participant. Passing on the access data is not allowed. Also, the temporary transfer to third parties is not permitted.
    • The right to use the transmitted access data, in particular with regard to any materials or video recordings provided, can only be exercised up to a maximum of 2 months after the program end. After expiration of this 2-months period, the access data will be deleted by Mannheim Business School (MBS). Before the expiration of this period, the participant may view the respective recorded course as often as desired and without time restriction.
    • If we have reasons to believe that the participant is abusing the right of use granted to him or that there is a violation of the terms of use, MBS reserves the right to change the participant’s access data as well as to partially or completely block the access or to prohibit the further use of the digital content.

    Group bookings

    For group bookings, please email Jodi Phillips with the names, email addresses, and telephone numbers  of the participants in the group.

    Cancellation Policy

    Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen.

    Cancellations received prior to two weeks before the event will be refunded, minus a $20 administration fee. From then onward no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.

    Online events: As you will still be able to watch the workshop stream after the event, we will not be offering any refunds.

    For any questions, please email SSA's Event Coordinator, Jodi Phillips.


    • 13 Oct 2022
    • 1:00 PM (AEDT)
    • Online
    Register

    SSA & NZSA Early Career and Student Statistician Networks invite you to the webinar presented by Dr Rheanna Mainzer.

    In this talk, She will share her experience navigating the transition from PhD student to bio-statistician. She will shed light on the day-to-day activities of a post-doc (both within a university and at a research institute) and describe some of the lessons she has learned along the way.

    Dr Rheanna Mainzer is a Postdoctoral Research Fellow in Biostatistics, currently investigating methods for handling missing data. She also provides statistical support for researchers in child health. Prior to this, she worked in the School of Mathematics and Statistics at The University of Melbourne. She has a Bachelor of Science, with honours, in Mathematics and Statistics, a Bachelor of Finance and a PhD in Statistics from La Trobe University.

    • 13 Oct 2022
    • (EDT)
    • 15 Oct 2022
    • (EDT)
    • DoubleTree by Hilton, Huntington, West Virginia, USA

    This international conference is being organized to provide a platform for researchers and practitioners to share and discuss recent advancements on statistical distributions and their applications, and to provide opportunities for collaborative work.

    The scopes of ICOSDA 2022 include, but not limited to statistical distributions and applications; statistical modeling; inference (frequentist or Bayesian) on statistical distributions; analysis of high dimensional data; generalized linear models; and statistical distributions in the era of global pandemic.

    For more information and to register please click here.

    • 20 Oct 2022
    • 9:00 AM (AEDT)
    • 21 Oct 2022
    • 5:00 PM (AEDT)
    • Macquarie University Sydney City Campus
    • 30
    Register

    The SSA and  the Australian Pharmaceutical Biostatistics Group (APBG) proudly offer this two day workshop, Estimands, Estimators and Estimates: Aligning target of estimation, method of estimation, and sensitivity analysis.

    The ICH E9(R1) Addendum on 'Estimands and Sensitivity Analysis in Clinical'  introduced a framework to align planning, design, conduct, analysis, and interpretation of clinical trials. When defining the clinical question of interest, clarity is needed about 'intercurrent events' that affect either the interpretation or the existence of the measurements associated with the clinical question of interest, such as discontinuation of assigned treatment, use of an additional or alternative treatment and terminal events such as death. The description of an estimand should reflect the clinical question of interest in respect of these intercurrent events, and the Addendum introduces strategies to reflect different questions of interest that might be posed.

    This two-day course will introduce the estimand framework according to the ICH E9(R1) Addendum. Using a generic example to illustrate the thinking process that aligns estimands and sensitivity analyses with trial objectives, we will provide an in-depth description of intercurrent events and various strategies for addressing them when defining the clinical question of interest. The choice of strategies can influence how more conventional attributes of a trial are reflected when describing the clinical question, for example the treatments, population or the variable (endpoint) of interest. For a given estimand, an aligned method of analysis, or estimator, should be implemented that is able to provide an estimate on which reliable interpretation can be based and which includes the handling of intercurrent events, missing data and sensitivity analyses. We will therefore also discuss how to identify and implement analyses approaches as well as sensitivity analyses that are aligned with a chosen estimand for different types of endpoints in longitudinal clinical trial settings.

    Course Outline

    This two-day short course will include eight lectures (one hour and 30 minutes for each), with four lectures on Day 1 (October 20) and four on Day 2 (October 21).

    Day 1:
    . Introduction, motivation and scope of the ICH E9(R1) Addendum
    . A framework to align planning, design, conduct, analysis and interpretation of clinical trials
    . Description, strategies and construction of estimands
    . Generic example to illustrate the thinking process that aligns estimands and sensitivity

    Day 2:
    . Gentle introduction to causal inference
    . Intercurrent events and missing data
    . Main analyses targeting estimands for different types of endpoints and strategies
    . Sensitivity and supplementary analysis in light of the estimand framework

    Learning Objectives

    This course will focus on estimands and related statistical methodologies that are commonly used in clinical trials. We will share our experiences and try to provide some guidance on their use in clinical trial practice. The target audience includes statisticians working in industry (pharmaceutical companies), academia (universities, medical centers, or research hospitals), or government (AIHW/TGA), and also graduate students who are interested in clinical trial methods. The difficulty level of the course is intermediate, at a second-year graduate course
    level.

    The learning objectives are three-fold: (1) to understand the fundamentals of the estimand framework and be able to apply it in clinical trials; (2) to identify an appropriate primary analysis method that targets the estimand of interest, fully aligned with the ICH E9(R1) Addendum; and (3) to implement appropriate main and sensitivity analyses.

    Catering will be including: Arrival Coffee, Morning tea, Lunch and
    Afternoon tea.

    Presenter Biography

    Dr. Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He has supported the methodological development in various areas of pharmaceutical statistics, including dose finding, multiple comparisons, estimands, and adaptive designs. Frank is an Adjunct Professor at the Hannover Medical School (Germany) and the Medical University of Vienna (Austria). He was a member of the ICH E9(R1) Expert Working Group on 'Estimands and sensitivity analysis in clinical trials' and currently serves on the ICH E20 Expert Working Group on 'Adaptive clinical trials'. Frank is a Fellow of the American Statistical Association.

    Cancellation Policy

    Cancellations received prior to Friday,October 14, 2022 will be refunded, minus a $20 administration fee. From then on wards no part of the registration fee will be refunded. However, registrations are transferable within the same organisation. Please advise any changes to events@statsoc.org.au.

    • 20 Oct 2022
    • 6:00 PM - 7:30 PM (AEDT)
    • University House at the Woodward, Level 10, Melbourne Law School, University of Melbourne, 185 Pelham Street, Carlton
    Register

    We are delighted to have Dr Dina Neiger, Chief Statistician at Social Research Centre, present this year's Belz Lecture. The lecture will be delivered in hybrid (online and in-person) at University House at the Woodward, Level 10, Melbourne Law School, University of Melbourne, 185 Pelham Street, Carlton. 

    Dina Neiger profile photo

    Following the lecture, we will hold the Belz Dinner. You can register for the dinner here. Tickets are limited for the dinner so please book early if you are intending to come! 

    More details to come soon.

    • 20 Oct 2022
    • 7:45 PM - 10:00 PM (AEDT)
    • University House at the Woodward, Level 10, Melbourne Law School, University of Melbourne, 185 Pelham Street, Carlton
    • 15
    Register

    The Victorian Branch of the Statistical Society of Australia warmly invites members and guests to the Belz Dinner, held at University House at the Woodward from 7:45PM. The dinner immediately follows the annual Belz Lecture, given this year by Dr Dina Neiger. Join us for the three-course dinner and beautiful views.

    Venue details

    The Belz dinner is back at University House at The Woodward - this is located at the top of the University of Melbourne's Law building. 


    If you have any dietary requirements please include them in your ticket registration.

    Tickets are limited and will only be available up until the 13th of October. To purchase a ticket at the member rates you need to be logged in to your account.

    • 25 Oct 2022
    • 2:00 PM
    • Online
     

    Linear Regression is a core technique as data analysts seek to understand the strength of the association between variables in a dataset, and specifically to understand:

    * Is there a relationship between variables (as seen by the P-value and the 95% Confidence Interval)?

    * How quickly does one variable change in response to another variable (the slope of the line or the regression coefficient)?

    * How much uncertainty is there in estimating the regression coefficient (the standard error)?

     

    In this webinar we introduce the technique of Linear Regression. Specifically we will look at:

    * Regression coefficients, standard errors, 95% confidence intervals, and P-values

    * Measures of model fit

    * Simple regression (with one variable as the predictor)

    * Multiple regression (with more than one variable as the predictor)

    * A brief comment about assumptions and diagnostics (how do we assess whether regression can be applied to our dataset)

     

    Prior knowledge

    No previous knowledge of regression or statistics is assumed of webinar participants, though participants with prior knowledge will be more able to appreciate the more advanced material within the webinar.

     

    About this webinar series

    Dr Mark Griffin, presents a free monthly webinar series introducing various methods used in Business Analytics (where these webinars are categorized into the themes of Analytics Strategy, Survey Design, Basic Statistics, Advanced Statistics, Data Mining, and Data Visualization). This webinar series is targeted at a broad audience who seek a better understanding of Business Analytics, and is open both to SSA members and non-members. These webinars are being jointly organized by the Statistical Society of Australia (Section for Business Analytics), the International Institute of Business Analysis (Special Interest Group for Business Analytics), and Insight Research Services Associated.

     

    If this webinar or webinar series would be of interest to you or any of your colleagues then please see https://www.insightrsa.com/upcoming-webinars.

     

    To stay in touch with the SSA Section for Business Analytics please consider joining linkedin.com/groups/13988720/
    • 9 Nov 2022
    • (AEDT)
    • 10 Nov 2022
    • (AEDT)
    • 2 sessions
    • Venue: Room 5.02, Marie Reay Teaching Building, The Australian National University
    • 21
    Register

    The SSA Canberra Branch warmly invites you to an in-person workshop on Time series analysis and forecasting using R , taught by Professor Rob J Hyndman (Monash University) and Associate Professor Bahman Rostami-Tabar (Cardiff University, UK). 


    **Places for this in-person workshop in Canberra are limited; please register ASAP to secure your place!**


    About the workshop:


    It is becoming increasingly common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some packages and methods that have been developed to handle the analysis of large collections of time series.

    On day 1, we will look at the tsibble data structure for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 will be tsibble, lubridate and feasts (along with the tidyverse of course).

    Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package. We will look at creating ensemble forecasts and hybrid forecasts, as well as some new forecasting methods that have performed well in large-scale forecasting competitions. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related.

    About the presenters:

    Rob J Hyndman FAA FASSA is Professor of Statistics in the Department of Econometrics and Business Statistics at Monash University. He is the author of over 200 research papers and 5 books in statistical science. He is an elected Fellow of both the Australian Academy of Science and the Academy of Social Sciences in Australia. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research, especially in the area of statistical forecasting. In 2021, he received the Pitman medal from the Statistical Society of Australia. For over 30 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organizations around the world. He has won awards for his research, teaching, consulting and graduate supervision.

    Bahman Rostami-Tabar is Associate Professor in Management Science & Analytics at Cardiff Business School, Cardiff University, UK. He is the founder and Chair of the Forecasting for Social Good initiatives sponsored by the International Institute of Forecasters. He has provided forecasting training in some of the world’s least developed countries, and for many organizations with social missions. He develops and uses management science and analytic tools and techniques to improve decision-making in healthcare, humanitarian operations and supply chain sectors.


    Target audience:

    This course will be appropriate for you if you answer yes to these questions:

    1. Do you already use R regularly, especially the tidyverse packages, or are willing to do suitable pre-course training in R and tidyverse?

    2. Do you need to analyse large collections of related time series?

    3. Would you like to learn how to use some new tidy tools for time series analysis including visualization, decomposition and forecasting?

    People who don't use R regularly, or don't know the tidyverse packages, are recommended to do the tutorials at learnr.numbat.space beforehand.


    Learning objectives:

    • How to wrangle time series data with familiar tidy tools.

    • How to compute time series features and visualize large collections of time series.

    • How to select a good forecasting algorithm for your time series.

    • How to ensure forecasts of a large collection of time series are coherent.


    Requirements:

    You will need to bring your own laptop to the workshop. Please make sure you have the required packages installed before you arrive. The following command will install the packages you need. 

    install.packages(c(

      "tidyverse",

      "fpp3",

      "GGally",

      "sugrrants"

    ))


    Say no more and sign me up!:

    The workshop is strictly in person, running from 9am to 5pm (with breaks in between) on November 9-10, 2022. The workshop will be run in Room 5.02, Marie Reay Teaching Building, The Australian National University in Canberra.


    Registration costs are as listed on this event website. Note registration includes morning and afternoon teas, but not lunch. Students enrolled in any tertiary institution can become SSA Student members for an annual fee of only $20. Early career people (less than 3 years after completing their degree) can become SSA Early Career members for $125. The cost of membership provides a substantial discount on the full workshop rate in both cases.


    Registrations close midnight Canberra time, Friday 28 October.


    Cancellations received prior to midnight Canberra time Tuesday 1 November 2022 will be refunded, minus an 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.


    If you have any questions, please SSA Canberra (ssacanberra@gmail.com) or Marie-Louise Rankin (eo@statsoc.org.au)

    • 10 Nov 2022
    • (UTC+04:00)
    • 15 Nov 2022
    • (UTC+04:00)
    • Dubai, United Arab Emirates

    The World Association for Public Opinion Research (WAPOR) will hold its 75th Annual Conference together with the WAPOR Asia Pacific 5th Annual Conference on 10-15 November 2022 in Dubai, United Arab Emirates. The WAPOR conference will convene before and overlap with the 5th WAPOR Asia Pacific regional conference.

    The conference committee welcomes proposals on topics related to public opinion broadly, especially those related to the conference theme.

    Conference Theme: 75 Years of Worldwide Public Opinion Research

    This year, WAPOR highlights the role and application of survey research in monitoring public opinion across the world since the end of WWII. This period has witnessed continuous and ongoing innovations in the conduct, analysis and interpretation of public opinion research, and this information is routinely disseminated across the globe. This research has become an essential element of a connected world and is now used to inform citizens, governments, private businesses, and academic researchers on countless topics. At the same time, public opinion research is not without its limitations and its critics.

    WAPOR welcomes proposal submissions that deal with these and related issues, in one of three conference formats including panels, research papers and posters. Relevant submissions that focus on all aspects of public opinion and survey research are encouraged. These topics include, but are not restricted to:

    • Good governance and public opinion research
    • Public opinion and public diplomacy
    • Public opinion and policymaking
    • Public opinion and survey research
    • Survey research application
    • News, media, journalism and public opinion
    • Panel, longitudinal and national monitoring surveys in policymaking
    • New technologies and national surveys: challenges in the changing times
    • New sources of information on public opinion and the use of social media
    • Political behavior, participation and culture in survey research
    • Methodological challenges and improvements in the areas of sampling, measurement, survey design and survey response or non-response
    • Challenges of comparative research and International Survey Projects
    • Cross-cultural concerns in data collection and measurement issues
    • Qualitative research in changing times
    • Big data, sentiment analysis and machine learning
    • Digitalization of societies and usage of new sources of information in survey research
    • Data visualization, new technologies and online surveys
    • Best practices for stakeholder research and expert surveys


    For more information on the submission process or the conference please click here.

    • 15 Nov 2022
    • (AEDT)
    • 17 Nov 2022
    • (AEDT)
    • Online + In-person (Perth)
    Register