Workshop: Analyzing Data Collected over Time: Challenges and Statistical Techniques

Analyzing Data Collected over Time: Challenges and Statistical Techniques

- in Kooperation mit dem Kompetenzzentrum für empirische Forschungsmethoden -

Monthly profits, inflation, and Gross Domestic Product are only some examples of data that are collected over time. These data present a challenge for you as researcher: subsequent observations are often dependent and analysing such data requires to go beyond classical regression analysis models.

The goal of this workshop is to provide participants with tools to analyse data collected over time and to guide them in finding the best methodology to answer different research questions.  
As participant, you are very welcome to present your own research problem and to bring your own dataset.

Target Group

Scientists from different disciplines who are interested in modeling data collected over time.

Participants should be familiar with descriptive statistics (central tendency and variability measures) and simple graphical analysis (histogram, boxplot, QQ-plot).  Previous exposure to linear regression (OLS) is preferable.
 

Objectives

Participants can expect to learn how to visualise such data, choose appropriate statistical tools and methods, and develop predictive models.

Content

The following topics will be covered:

• Visualising and describing data collected over time
• Challenges in modeling such data
• Panel data
• Time series analysis
• Real data applications

The time dedicated to each topic will depend on the participants’ background and research interests.

Methods

The workshop will consist of a mixture of lectures and practical sessions. Theory and methodology presented in the lectures will be complemented by hands-on data analyses using the statistical software R.

Bring your own laptop: Participants need to bring their own laptop with R and RStudio installed.
R is open source. For private laptops, R can be downloaded at https://www.r-project.org/. RStudio at https://www.rstudio.com/products/rstudio/download/.
For WU-owned laptops, you need to download the software R and RStudio from the Software Center.


Bring your own dataset: Participants are welcome to bring their own datasets for discussion and analysis.

Note: No prior programming experience is required. R code for all the examples will be provided.

 
Location, Date and Time

Thursday, 06 December 2018, 9.00 am - 5.00 pm

Friday, 07 December 2018, 9.00 am - 5.00 pm

All registered participants will be informed of the location by email one week before the event.

Fees and Registration

This workshop is open to a limited number of participants. The registration deadline is 22 November 2018.

Please note that you should notify us before the registration deadline if you are unable to attend the workshop. Otherwise the full fee will be charged.
Please let the Personnel Development and Planning Office know if you will not be attending. There are often waiting lists for the workshops, and this will give one of your colleagues the chance to attend. In addition, if we can fill the vacancy with someone from the waiting list, no cancellation charges will apply for you.

To sign up for this workshop, please click "Register" at the bottom of this page. For any further information please contact Elke Rittersbacher (elke.rittersbacher@wu.ac.at, Ext. 4448).

The course fee is €40.

Dr. Annalisa Cadonna   Dr. Annalisa Cadonna is an Assistant Professor at the Institute of Statistics and Mathematics at Vienna University of Economics and Business. She earned her Ph.D. in Applied Mathematics and Statistics from University of California, Santa Cruz. Her research focuses on time series analysis using Bayesian methods.